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Опубліковано: 4 травня 2024

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Статті в журналах з теми "Similarity metric learning"

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Loriette, Antoine, Wanyu Liu, Frédéric Bevilacqua, and Baptiste Caramiaux. "Describing movement learning using metric learning." PLOS ONE 18, no. 2 (February 3, 2023): e0272509. http://dx.doi.org/10.1371/journal.pone.0272509.

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Анотація:
Analysing movement learning can rely on human evaluation, e.g. annotating video recordings, or on computing means in applying metrics on behavioural data. However, it remains challenging to relate human perception of movement similarity to computational measures that aim at modelling such similarity. In this paper, we propose a metric learning method bridging the gap between human ratings of movement similarity in a motor learning task and computational metric evaluation on the same task. It applies metric learning on a Dynamic Time Warping algorithm to derive an optimal set of movement features that best explain human ratings. We evaluated this method on an existing movement dataset, which comprises videos of participants practising a complex gesture sequence toward a target template, as well as the collected data that describes the movements. We show that it is possible to establish a linear relationship between human ratings and our learned computational metric. This learned metric can be used to describe the most salient temporal moments implicitly used by annotators, as well as movement parameters that correlate with motor improvements in the dataset. We conclude with possibilities to generalise this method for designing computational tools dedicated to movement annotation and evaluation of skill learning.
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2

Tao, Tao, Qianqian Wang, Yue Ruan, Xue Li, and Xiujun Wang. "Graph Embedding with Similarity Metric Learning." Symmetry 15, no. 8 (August 21, 2023): 1618. http://dx.doi.org/10.3390/sym15081618.

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Анотація:
Graph embedding transforms high-dimensional graphs into a lower-dimensional vector space while preserving their structural information and properties. Context-sensitive graph embedding, in particular, performs well in tasks such as link prediction and ranking recommendations. However, existing context-sensitive graph embeddings have limitations: they require additional information, depend on community algorithms to capture multiple contexts, or fail to capture sufficient structural information. In this paper, we propose a novel Graph Embedding with Similarity Metric Learning (GESML). The core of GESML is to learn the optimal graph structure using an attention-based symmetric similarity metric function and establish association relationships between nodes through top-k pooling. Its primary advantage lies in not requiring additional features or multiple contexts, only using the symmetric similarity metric function and pooling operations to encode sufficient topological information for each node. Experimental results on three datasets involving link prediction and node-clustering tasks demonstrate that GESML significantly improves learning for all challenging tasks relative to a state-of-the-art (SOTA) baseline.
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3

LE, Yi-ze, Yong FENG, Da-jiang LIU, and Bao-hua QIANG. "Adversarial Metric Learning with Naive Similarity Discriminator." IEICE Transactions on Information and Systems E103.D, no. 6 (June 1, 2020): 1406–13. http://dx.doi.org/10.1587/transinf.2019edp7278.

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4

Li, Yujiang, Chun Ding, and Zhili Zhou. "Vehicle Matching Based on Similarity Metric Learning." Journal of New Media 4, no. 1 (2022): 51–58. http://dx.doi.org/10.32604/jnm.2022.028775.

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5

Wei, Zeqiang, Min Xu, Lin Geng, Haoming Liu, and Hua Yin. "Adversarial Similarity Metric Learning for Kinship Verification." IEEE Access 7 (2019): 100029–35. http://dx.doi.org/10.1109/access.2019.2929939.

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Cao, Qiong, Zheng-Chu Guo, and Yiming Ying. "Generalization bounds for metric and similarity learning." Machine Learning 102, no. 1 (June 20, 2015): 115–32. http://dx.doi.org/10.1007/s10994-015-5499-7.

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7

Lowe, David G. "Similarity Metric Learning for a Variable-Kernel Classifier." Neural Computation 7, no. 1 (January 1995): 72–85. http://dx.doi.org/10.1162/neco.1995.7.1.72.

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Анотація:
Nearest-neighbor interpolation algorithms have many useful properties for applications to learning, but they often exhibit poor generalization. In this paper, it is shown that much better generalization can be obtained by using a variable interpolation kernel in combination with conjugate gradient optimization of the similarity metric and kernel size. The resulting method is called variable-kernel similarity metric (VSM) learning. It has been tested on several standard classification data sets, and on these problems it shows better generalization than backpropagation and most other learning methods. The number of parameters that must be determined through optimization are orders of magnitude less than for backpropagation or radial basis function (RBF) networks, which may indicate that the method better captures the essential degrees of variation in learning. Other features of VSM learning are discussed that make it relevant to models for biological learning in the brain.
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8

Garcia, Noa, and George Vogiatzis. "Learning non-metric visual similarity for image retrieval." Image and Vision Computing 82 (February 2019): 18–25. http://dx.doi.org/10.1016/j.imavis.2019.01.001.

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9

Wang, Huibing, Lin Feng, Jing Zhang, and Yang Liu. "Semantic Discriminative Metric Learning for Image Similarity Measurement." IEEE Transactions on Multimedia 18, no. 8 (August 2016): 1579–89. http://dx.doi.org/10.1109/tmm.2016.2569412.

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10

Zhang, Lei, and David Zhang. "MetricFusion: Generalized metric swarm learning for similarity measure." Information Fusion 30 (July 2016): 80–90. http://dx.doi.org/10.1016/j.inffus.2015.12.004.

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Більше джерел

Дисертації з теми "Similarity metric learning"

1

Cao, Qiong. "Some topics on similarity metric learning." Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18662.

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Анотація:
The success of many computer vision problems and machine learning algorithms critically depends on the quality of the chosen distance metrics or similarity functions. Due to the fact that the real-data at hand is inherently task- and data-dependent, learning an appropriate distance metric or similarity function from data for each specific task is usually superior to the default Euclidean distance or cosine similarity. This thesis mainly focuses on developing new metric and similarity learning models for three tasks: unconstrained face verification, person re-identification and kNN classification. Unconstrained face verification is a binary matching problem, the target of which is to predict whether two images/videos are from the same person or not. Concurrently, person re-identification handles pedestrian matching and ranking across non-overlapping camera views. Both vision problems are very challenging because of the large transformation differences in images or videos caused by pose, expression, occlusion, problematic lighting and viewpoint. To address the above concerns, two novel methods are proposed. Firstly, we introduce a new dimensionality reduction method called Intra-PCA by considering the robustness to large transformation differences. We show that Intra-PCA significantly outperforms the classic dimensionality reduction methods (e.g. PCA and LDA). Secondly, we propose a novel regularization framework called Sub-SML to learn distance metrics and similarity functions for unconstrained face verifica- tion and person re-identification. The main novelty of our formulation is to incorporate both the robustness of Intra-PCA to large transformation variations and the discriminative power of metric and similarity learning, a property that most existing methods do not hold. Working with the task of kNN classification which relies a distance metric to identify the nearest neighbors, we revisit some popular existing methods for metric learning and develop a general formulation called DMLp for learning a distance metric from data. To obtain the optimal solution, a gradient-based optimization algorithm is proposed which only needs the computation of the largest eigenvector of a matrix per iteration. Although there is a large number of studies devoted to metric/similarity learning based on different objective functions, few studies address the generalization analysis of such methods. We describe a novel approch for generalization analysis of metric/similarity learning which can deal with general matrix regularization terms including the Frobenius norm, sparse L1-norm, mixed (2, 1)-norm and trace-norm. The novel models developed in this thesis are evaluated on four challenging databases: the Labeled Faces in the Wild dataset for unconstrained face verification in still images; the YouTube Faces database for video-based face verification in the wild; the Viewpoint Invariant Pedestrian Recognition database for person re-identification; the UCI datasets for kNN classification. Experimental results show that the proposed methods yield competitive or state-of-the-art performance.
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2

Cuan, Bonan. "Deep similarity metric learning for multiple object tracking." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI065.

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Анотація:
Le suivi d’objets multiples dans une scène est une tâche importante dans le domaine de la vision par ordinateur, et présente toujours de très nombreux verrous. Les objets doivent être détectés et distingués les uns des autres de manière continue et simultanée. Les approches «suivi par détection» sont largement utilisées, où la détection des objets est d’abord réalisée sur toutes les frames, puis le suivi est ramené à un problème d’association entre les détections d’un même objet et les trajectoires identifiées. La plupart des algorithmes de suivi associent des modèles de mouvement et des modèles d’apparence. Dans cette thèse, nous proposons un modèle de ré-identification basé sur l’apparence et utilisant l’apprentissage de métrique de similarité. Nous faisons tout d’abord appel à un réseau siamois profond pour apprendre un maping de bout en bout, des images d’entrée vers un espace de caractéristiques où les objets sont mieux discriminés. De nombreuses configurations sont évaluées, afin d’en déduire celle offrant les meilleurs scores. Le modèle ainsi obtenu atteint des résultats de ré-identification satisfaisants comparables à l’état de l’art. Ensuite, notre modèle est intégré dans un système de suivi d’objets multiples pour servir de guide d’apparence pour l’association des objets. Un modèle d’apparence est établi pour chaque objet détecté s’appuyant sur le modèle de ré-identification. Les similarités entre les objets détectés sont alors exploitées pour la classification. Par ailleurs, nous avons étudié la coopération et les interférences entre les modèles d’apparence et de mouvement dans le processus de suivi. Un couplage actif entre ces 2 modèles est proposé pour améliorer davantage les performances du suivi, et la contribution de chacun d’eux est estimée en continue. Les expérimentations menées dans le cadre du benchmark «Multiple Object Tracking Challenge» ont prouvé l’efficacité de nos propositions et donné de meilleurs résultats de suivi que l’état de l’art
Multiple object tracking, i.e. simultaneously tracking multiple objects in the scene, is an important but challenging visual task. Objects should be accurately detected and distinguished from each other to avoid erroneous trajectories. Since remarkable progress has been made in object detection field, “tracking-by-detection” approaches are widely adopted in multiple object tracking research. Objects are detected in advance and tracking reduces to an association problem: linking detections of the same object through frames into trajectories. Most tracking algorithms employ both motion and appearance models for data association. For multiple object tracking problems where exist many objects of the same category, a fine-grained discriminant appearance model is paramount and indispensable. Therefore, we propose an appearance-based re-identification model using deep similarity metric learning to deal with multiple object tracking in mono-camera videos. Two main contributions are reported in this dissertation: First, a deep Siamese network is employed to learn an end-to-end mapping from input images to a discriminant embedding space. Different metric learning configurations using various metrics, loss functions, deep network structures, etc., are investigated, in order to determine the best re-identification model for tracking. In addition, with an intuitive and simple classification design, the proposed model achieves satisfactory re-identification results, which are comparable to state-of-the-art approaches using triplet losses. Our approach is easy and fast to train and the learned embedding can be readily transferred onto the domain of tracking tasks. Second, we integrate our proposed re-identification model in multiple object tracking as appearance guidance for detection association. For each object to be tracked in a video, we establish an identity-related appearance model based on the learned embedding for re-identification. Similarities among detected object instances are exploited for identity classification. The collaboration and interference between appearance and motion models are also investigated. An online appearance-motion model coupling is proposed to further improve the tracking performance. Experiments on Multiple Object Tracking Challenge benchmark prove the effectiveness of our modifications, with a state-of-the-art tracking accuracy
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3

Zheng, Lilei. "Triangular similarity metric learning : A siamese architecture approach." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI045/document.

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Анотація:
Dans de nombreux problèmes d’apprentissage automatique et de reconnaissance des formes, il y a toujours un besoin de fonctions métriques appropriées pour mesurer la distance ou la similarité entre des données. La fonction métrique est une fonction qui définit une distance ou une similarité entre chaque paire d’éléments d’un ensemble de données. Dans cette thèse, nous proposons une nouvelle methode, Triangular Similarity Metric Learning (TSML), pour spécifier une fonction métrique de données automatiquement. Le système TSML proposée repose une architecture Siamese qui se compose de deux sous-systèmes identiques partageant le même ensemble de paramètres. Chaque sous-système traite un seul échantillon de données et donc le système entier reçoit une paire de données en entrée. Le système TSML comprend une fonction de coût qui définit la relation entre chaque paire de données et une fonction de projection permettant l’apprentissage des formes de haut niveau. Pour la fonction de coût, nous proposons d’abord la similarité triangulaire (Triangular Similarity), une nouvelle similarité métrique qui équivaut à la similarité cosinus. Sur la base d’une version simplifiée de la similarité triangulaire, nous proposons la fonction triangulaire (the triangular loss) afin d’effectuer l’apprentissage de métrique, en augmentant la similarité entre deux vecteurs dans la même classe et en diminuant la similarité entre deux vecteurs de classes différentes. Par rapport aux autres distances ou similarités, la fonction triangulaire et sa fonction gradient nous offrent naturellement une interprétation géométrique intuitive et intéressante qui explicite l’objectif d’apprentissage de métrique. En ce qui concerne la fonction de projection, nous présentons trois fonctions différentes: une projection linéaire qui est réalisée par une matrice simple, une projection non-linéaire qui est réalisée par Multi-layer Perceptrons (MLP) et une projection non-linéaire profonde qui est réalisée par Convolutional Neural Networks (CNN). Avec ces fonctions de projection, nous proposons trois systèmes de TSML pour plusieurs applications: la vérification par paires, l’identification d’objet, la réduction de la dimensionnalité et la visualisation de données. Pour chaque application, nous présentons des expérimentations détaillées sur des ensembles de données de référence afin de démontrer l’efficacité de notre systèmes de TSML
In many machine learning and pattern recognition tasks, there is always a need for appropriate metric functions to measure pairwise distance or similarity between data, where a metric function is a function that defines a distance or similarity between each pair of elements of a set. In this thesis, we propose Triangular Similarity Metric Learning (TSML) for automatically specifying a metric from data. A TSML system is loaded in a siamese architecture which consists of two identical sub-systems sharing the same set of parameters. Each sub-system processes a single data sample and thus the whole system receives a pair of data as the input. The TSML system includes a cost function parameterizing the pairwise relationship between data and a mapping function allowing the system to learn high-level features from the training data. In terms of the cost function, we first propose the Triangular Similarity, a novel similarity metric which is equivalent to the well-known Cosine Similarity in measuring a data pair. Based on a simplified version of the Triangular Similarity, we further develop the triangular loss function in order to perform metric learning, i.e. to increase the similarity between two vectors in the same class and to decrease the similarity between two vectors of different classes. Compared with other distance or similarity metrics, the triangular loss and its gradient naturally offer us an intuitive and interesting geometrical interpretation of the metric learning objective. In terms of the mapping function, we introduce three different options: a linear mapping realized by a simple transformation matrix, a nonlinear mapping realized by Multi-layer Perceptrons (MLP) and a deep nonlinear mapping realized by Convolutional Neural Networks (CNN). With these mapping functions, we present three different TSML systems for various applications, namely, pairwise verification, object identification, dimensionality reduction and data visualization. For each application, we carry out extensive experiments on popular benchmarks and datasets to demonstrate the effectiveness of the proposed systems
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4

Zhang, Hauyi. "Similarity Search in Continuous Data with Evolving Distance Metric." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1253.

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Анотація:
Similarity search is a task fundamental to many machine learning and data analytics applications, where distance metric learning plays an important role. However, since modern online applications continuously produce objects with new characteristics which tend to change over time, state-of-the-art similarity search using distance metric learning methods tends to fail when deployed in such applications without taking the change into consideration. In this work, we propose a Distance Metric Learning-based Continuous Similarity Search approach (CSS for short) to account for the dynamic nature of such data. CSS system adopts an online metric learning model to achieve distance metric evolving to adapt the dynamic nature of continuous data without large latency. To improve the accuracy of online metric learning model, a compact labeled dataset which is representative of the updated data is dynamically updated. Also, to accelerate similarity search, CSS includes an online maintained Locality Sensitive Hashing index to accelerate the similarity search. One, our labeled data update strategy progressively enriches the labeled data to assure continued representativeness, yet without excessively growing its size to ensure that the computation costs of metric learning remain bounded. Two, our continuous distance metric learning strategy ensures that each update only requires one linear time k-NN search in contrast to the cubic time complexity of relearning the distance metric from scratch. Three, our LSH update mechanism leverages our theoretical insight that the LSH built based on the original distance metric is equally effective in supporting similarity search using the new distance metric as long as the transform matrix learned for the new distance metric is reversible. This important observation empowers CSS to avoid the modification of LSH in most cases. Our experimental study using real-world public datasets and large synthetic datasets confirms the effectiveness of CSS in improving the accuracy of classification and information retrieval tasks. Also, CSS achieves 3 orders of magnitude speedup of our incremental distance metric learning strategy (and its three underlying components) over the state-of-art methods.
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5

Forssell, Melker, and Gustav Janér. "Product Matching Using Image Similarity." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413481.

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Анотація:
PriceRunner is an online shopping comparison company. To maintain up-todate prices, PriceRunner has to process large amounts of data every day. The processing of the data includes matching unknown products, referred to as offers, to known products. Offer data includes information about the product such as: title, description, price and often one image of the product. PriceRunner has previously implemented a textual-based machine learning (ML) model, but is also looking for new approaches to complement the current product matching system. The objective of this master’s thesis is to investigate the potential of using an image-based ML model for product matching. Our method uses a similarity learning approach where the network learns to recognise the similarity between images. To achieve this, a siamese neural network was trained with the triplet loss function. The network is trained to map similar images closer together and dissimilar images further apart in a vector space. This approach is often used for face recognition, where there is an extensive amount of classes and a limited amount of images per class, and new classes are frequently added. This is also the case for the image data used in this thesis project. A general model was trained on images from the Clothing and Accessories hierarchy, one of the 16 toplevel hierarchies at PriceRunner, consisting of 17 product categories. The results varied between each product category. Some categories proved to be less suitable for image-based classification while others excelled. The model handles new classes relatively well without any, or with briefer, retraining. It was concluded that there is potential in using images to complement the current product matching system at PriceRunner.
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6

Michel, Fabrice. "Multi-Modal Similarity Learning for 3D Deformable Registration of Medical Images." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-01005141.

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Анотація:
Even though the prospect of fusing images issued by different medical imagery systems is highly contemplated, the practical instantiation of it is subject to a theoretical hurdle: the definition of a similarity between images. Efforts in this field have proved successful for select pairs of images; however defining a suitable similarity between images regardless of their origin is one of the biggest challenges in deformable registration. In this thesis, we chose to develop generic approaches that allow the comparison of any two given modality. The recent advances in Machine Learning permitted us to provide innovative solutions to this very challenging problem. To tackle the problem of comparing incommensurable data we chose to view it as a data embedding problem where one embeds all the data in a common space in which comparison is possible. To this end, we explored the projection of one image space onto the image space of the other as well as the projection of both image spaces onto a common image space in which the comparison calculations are conducted. This was done by the study of the correspondences between image features in a pre-aligned dataset. In the pursuit of these goals, new methods for image regression as well as multi-modal metric learning methods were developed. The resulting learned similarities are then incorporated into a discrete optimization framework that mitigates the need for a differentiable criterion. Lastly we investigate on a new method that discards the constraint of a database of images that are pre-aligned, only requiring data annotated (segmented) by a physician. Experiments are conducted on two challenging medical images data-sets (Pre-Aligned MRI images and PET/CT images) to justify the benefits of our approach.
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Eriksson, Louise. "An experimental investigation of the relation between learning and separability in spatial representations." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-622.

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Анотація:

One way of modeling human knowledge is by using multidimensional spaces, in which an object is represented as a point in the space, and the distances among the points reflect the similarities among the represented objects. The distances are measured with some metric, commonly some instance of the Minkowski metric. The instances differ with the magnitude of the so-called r-parameter. The instances most commonly mentioned in the literature are the ones where r equals 1, 2 and infinity.

Cognitive scientists have found out that different metrics are suited to describe different dimensional combinations. From these findings an important distinction between integral and separable dimensions has been stated (Garner, 1974). Separable dimensions, e.g. size and form, are best described by the city-block metric, where r equals 1, and integral dimensions, such as the color dimensions, are best described by the Euclidean metric, where r equals 2. Developmental psychologists have formulated a hypothesis saying that small children perceive many dimensional combinations as integral whereas adults perceive the same combinations as separable. Thus, there seems to be a shift towards increasing separability with age or maturity.

Earlier experiments show the same phenomenon in adult short-term learning with novel stimuli. In these experiments, the stimuli were first perceived as rather integral and were then turning more separable, indicated by the Minkowski-r. This indicates a shift towards increasing separability with familiarity or skill.

This dissertation aims at investigating the generality of this phenomenon. Five similarity-rating experiments are conducted, for which the best fitting metric for the first half of the session is compared to the last half of the session. If the Minkowski-r is lower for the last half compared to the first half, it is considered to indicate increasing separability.

The conclusion is that the phenomenon of increasing separability during short-term learning cannot be found in these experiments, at least not given the operational definition of increasing separability as a function of a decreasing Minkowski-r. An alternative definition of increasing separability is suggested, where an r-value ‘retreating’ 2.0 indicates increasing separability, i.e. when the r-value of the best fitting metric for the last half of a similarity-rating session is further away from 2.0 compared to the first half of the session.

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8

Qamar, Ali Mustafa. "Mesures de similarité et cosinus généralisé : une approche d'apprentissage supervisé fondée sur les k plus proches voisins." Phd thesis, Grenoble, 2010. http://www.theses.fr/2010GRENM083.

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Анотація:
Les performances des algorithmes d'apprentissage automatique dépendent de la métrique utilisée pour comparer deux objets, et beaucoup de travaux ont montré qu'il était préférable d'apprendre une métrique à partir des données plutôt que se reposer sur une métrique simple fondée sur la matrice identité. Ces résultats ont fourni la base au domaine maintenant qualifié d'apprentissage de métrique. Toutefois, dans ce domaine, la très grande majorité des développements concerne l'apprentissage de distances. Toutefois, dans certaines situations, il est préférable d'utiliser des similarités (par exemple le cosinus) que des distances. Il est donc important, dans ces situations, d'apprendre correctement les métriques à la base des mesures de similarité. Il n'existe pas à notre connaissance de travaux complets sur le sujet, et c'est une des motivations de cette thèse. Dans le cas des systèmes de filtrage d'information où le but est d'affecter un flot de documents à un ou plusieurs thèmes prédéfinis et où peu d'information de supervision est disponible, des seuils peuvent être appris pour améliorer les mesures de similarité standard telles que le cosinus. L'apprentissage de tels seuils représente le premier pas vers un apprentissage complet des mesures de similarité. Nous avons utilisé cette stratégie au cours des campagnes CLEF INFILE 2008 et 2009, en proposant des versions en ligne et batch de nos algorithmes. Cependant, dans le cas où l'on dispose de suffisamment d'information de supervision, comme en catégorisation, il est préférable d'apprendre des métriques complètes, et pas seulement des seuils. Nous avons développé plusieurs algorithmes qui visent à ce but dans le cadre de la catégorisation à base de k plus proches voisins. Nous avons tout d'abord développé un algorithme, SiLA, qui permet d'apprendre des similarités non contraintes (c'est-à-dire que la mesure peut être symétrique ou non). SiLA est une extension du perceptron par vote et permet d'apprendre des similarités qui généralisent le cosinus, ou les coefficients de Dice ou de Jaccard. Nous avons ensuite comparé SiLA avec RELIEF, un algorithme standard de re-pondération d'attributs, dont le but n'est pas sans lien avec l'apprentissage de métrique. En effet, il a récemment été suggéré par Sun et Wu que RELIEF pouvait être considéré comme un algorithme d'apprentissage de métrique avec pour fonction objectif une approximation de la fonction de perte 0-1. Nous montrons ici que cette approximation est relativement mauvaise et peut être avantageusement remplacée par une autre, qui conduit à un algorithme dont les performances sont meilleures. Nous nous sommes enfin intéressés à une extension directe du cosinus, extension définie comme la forme normalisée d'un produit scalaire dans un espace projeté. Ce travail a donné lieu à l'algorithme gCosLA. Nous avons testé tous nos algorithmes sur plusieurs bases de données. Un test statistique, le s-test, est utilisé pour déterminer si les différences entre résultats sont significatives ou non. GCosLA est l'algorithme qui a fourni les meilleurs résultats. De plus, SiLA et gCosLA se comparent avantageusement à plusieurs algorithmes standard, ce qui illustre leur bien fondé
Almost all machine learning problems depend heavily on the metric used. Many works have proved that it is a far better approach to learn the metric structure from the data rather than assuming a simple geometry based on the identity matrix. This has paved the way for a new research theme called metric learning. Most of the works in this domain have based their approaches on distance learning only. However some other works have shown that similarity should be preferred over distance metrics while dealing with textual datasets as well as with non-textual ones. Being able to efficiently learn appropriate similarity measures, as opposed to distances, is thus of high importance for various collections. If several works have partially addressed this problem for different applications, no previous work is known which has fully addressed it in the context of learning similarity metrics for kNN classification. This is exactly the focus of the current study. In the case of information filtering systems where the aim is to filter an incoming stream of documents into a set of predefined topics with little supervision, cosine based category specific thresholds can be learned. Learning such thresholds can be seen as a first step towards learning a complete similarity measure. This strategy was used to develop Online and Batch algorithms for information filtering during the INFILE (Information Filtering) track of the CLEF (Cross Language Evaluation Forum) campaign during the years 2008 and 2009. However, provided enough supervised information is available, as is the case in classification settings, it is usually beneficial to learn a complete metric as opposed to learning thresholds. To this end, we developed numerous algorithms for learning complete similarity metrics for kNN classification. An unconstrained similarity learning algorithm called SiLA is developed in which case the normalization is independent of the similarity matrix. SiLA encompasses, among others, the standard cosine measure, as well as the Dice and Jaccard coefficients. SiLA is an extension of the voted perceptron algorithm and allows to learn different types of similarity functions (based on diagonal, symmetric or asymmetric matrices). We then compare SiLA with RELIEF, a well known feature re-weighting algorithm. It has recently been suggested by Sun and Wu that RELIEF can be seen as a distance metric learning algorithm optimizing a cost function which is an approximation of the 0-1 loss. We show here that this approximation is loose, and propose a stricter version closer to the the 0-1 loss, leading to a new, and better, RELIEF-based algorithm for classification. We then focus on a direct extension of the cosine similarity measure, defined as a normalized scalar product in a projected space. The associated algorithm is called generalized Cosine simiLarity Algorithm (gCosLA). All of the algorithms are tested on many different datasets. A statistical test, the s-test, is employed to assess whether the results are significantly different. GCosLA performed statistically much better than SiLA on many of the datasets. Furthermore, SiLA and gCosLA were compared with many state of the art algorithms, illustrating their well-foundedness
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Bäck, Jesper. "Domain similarity metrics for predicting transfer learning performance." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153747.

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Анотація:
The lack of training data is a common problem in machine learning. One solution to thisproblem is to use transfer learning to remove or reduce the requirement of training data.Selecting datasets for transfer learning can be difficult however. As a possible solution, thisstudy proposes the domain similarity metrics document vector distance (DVD) and termfrequency-inverse document frequency (TF-IDF) distance. DVD and TF-IDF could aid inselecting datasets for good transfer learning when there is no data from the target domain.The simple metric, shared vocabulary, is used as a baseline to check whether DVD or TF-IDF can indicate a better choice for a fine-tuning dataset. SQuAD is a popular questionanswering dataset which has been proven useful for pre-training models for transfer learn-ing. The results were therefore measured by pre-training a model on the SQuAD datasetand fine-tuning on a selection of different datasets. The proposed metrics were used tomeasure the similarity between the datasets to see whether there was a correlation betweentransfer learning effect and similarity. The results found a clear relation between a smalldistance according to the DVD metric and good transfer learning. This could prove usefulfor a target domain without training data, a model could be trained on a big dataset andfine-tuned on a small dataset that is very similar to the target domain. It was also foundthat even small amount of training data from the target domain can be used to fine-tune amodel pre-trained on another domain of data, achieving better performance compared toonly training on data from the target domain.
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Ferns, Norman Francis. "State-similarity metrics for continuous Markov decision processes." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103383.

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Анотація:
In recent years, various metrics have been developed for measuring the similarity of states in probabilistic transition systems (Desharnais et al., 1999; van Breugel & Worrell, 2001a). In the context of Markov decision processes, we have devised metrics providing a robust quantitative analogue of bisimulation. Most importantly, the metric distances can be used to bound the differences in the optimal value function that is integral to reinforcement learning (Ferns et al. 2004; 2005). More recently, we have discovered an efficient algorithm to calculate distances in the case of finite systems (Ferns et al., 2006). In this thesis, we seek to properly extend state-similarity metrics to Markov decision processes with continuous state spaces both in theory and in practice. In particular, we provide the first distance-estimation scheme for metrics based on bisimulation for continuous probabilistic transition systems. Our work, based on statistical sampling and infinite dimensional linear programming, is a crucial first step in real-world planning; many practical problems are continuous in nature, e.g. robot navigation, and often a parametric model or crude finite approximation does not suffice. State-similarity metrics allow us to reason about the quality of replacing one model with another. In practice, they can be used directly to aggregate states.
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Частини книг з теми "Similarity metric learning"

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Duffner, Stefan, Christophe Garcia, Khalid Idrissi, and Atilla Baskurt. "Similarity Metric Learning." In Multi-faceted Deep Learning, 103–25. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-74478-6_5.

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Hoffer, Elad, and Nir Ailon. "Deep Metric Learning Using Triplet Network." In Similarity-Based Pattern Recognition, 84–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24261-3_7.

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Wu, Xiang, Zhi-Guo Shi, and Lei Liu. "Quasi Cosine Similarity Metric Learning." In Computer Vision - ACCV 2014 Workshops, 194–205. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16634-6_15.

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Kliper-Gross, Orit, Tal Hassner, and Lior Wolf. "One Shot Similarity Metric Learning for Action Recognition." In Similarity-Based Pattern Recognition, 31–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24471-1_3.

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Naudé, Johannes J., Michaël A. van Wyk, and Barend J. van Wyk. "Generalized Variable-Kernel Similarity Metric Learning." In Lecture Notes in Computer Science, 788–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27868-9_86.

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Carrara, Fabio, Claudio Gennaro, Fabrizio Falchi, and Giuseppe Amato. "Learning Distance Estimators from Pivoted Embeddings of Metric Objects." In Similarity Search and Applications, 361–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60936-8_28.

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Nguyen, Hieu V., and Li Bai. "Cosine Similarity Metric Learning for Face Verification." In Computer Vision – ACCV 2010, 709–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19309-5_55.

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Ahmadzadeh, Azim, Yang Chen, Krishna Rukmini Puthucode, Ruizhe Ma, and Rafal A. Angryk. "TS-MIoU: A Time Series Similarity Metric Without Mapping." In Machine Learning and Knowledge Discovery in Databases, 87–102. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26422-1_6.

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Ricci, Francesco, and Paolo Avesani. "Learning a local similarity metric for case-based reasoning." In Case-Based Reasoning Research and Development, 301–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60598-3_27.

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van den Berg, Sophie, and Marwan Hassani. "On Inferring a Meaningful Similarity Metric for Customer Behaviour." In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 234–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86517-7_15.

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Тези доповідей конференцій з теми "Similarity metric learning"

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Xiaoqiang Zhu, Pinghua Gong, Zengshun Zhao, and Changshui Zhang. "Learning similarity metric with SVM." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252829.

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Cao, Qiong, Yiming Ying, and Peng Li. "Similarity Metric Learning for Face Recognition." In 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.299.

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Xu, Xinyi, Huanhuan Cao, Yanhua Yang, Erkun Yang, and Cheng Deng. "Zero-shot Metric Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/555.

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Анотація:
In this work, we tackle the zero-shot metric learning problem and propose a novel method abbreviated as ZSML, with the purpose to learn a distance metric that measures the similarity of unseen categories (even unseen datasets). ZSML achieves strong transferability by capturing multi-nonlinear yet continuous relation among data. It is motivated by two facts: 1) relations can be essentially described from various perspectives; and 2) traditional binary supervision is insufficient to represent continuous visual similarity. Specifically, we first reformulate a collection of specific-shaped convolutional kernels to combine data pairs and generate multiple relation vectors. Furthermore, we design a new cross-update regression loss to discover continuous similarity. Extensive experiments including intra-dataset transfer and inter-dataset transfer on four benchmark datasets demonstrate that ZSML can achieve state-of-the-art performance.
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Wei Wang and Bin-Xing Fang. "A grid execution environment similarity metric." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527458.

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Lee, Jongpil, Nicholas J. Bryan, Justin Salamon, Zeyu Jin, and Juhan Nam. "Disentangled Multidimensional Metric Learning for Music Similarity." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053442.

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Fang, Yuan, Yan Yan, Si Chen, Hanzi Wang, and Chang Shu. "Sparse similarity metric learning for kinship verification." In 2016 Visual Communications and Image Processing (VCIP). IEEE, 2016. http://dx.doi.org/10.1109/vcip.2016.7805462.

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Lilei Zheng, Khalid Idrissi, Christophe Garcia, Stefan Duffner, and Atilla Baskurt. "Triangular similarity metric learning for face verification." In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE, 2015. http://dx.doi.org/10.1109/fg.2015.7163085.

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Jin, Guoxin, and Thrasyvoulos N. Pappas. "Building structural similarity database for metric learning." In IS&T/SPIE Electronic Imaging, edited by Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Huib de Ridder. SPIE, 2015. http://dx.doi.org/10.1117/12.2079392.

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Zheng, Lilei, Khalid Idrissi, Christophe Garcia, Stefan Duffner, and Atilla Baskurt. "Logistic similarity metric learning for face verification." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178311.

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Zhang, Kaizhong. "Similarity metric induced metrics with application in machine learning and bioinformatics." In 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2016. http://dx.doi.org/10.1109/icci-cc.2016.7862048.

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Звіти організацій з теми "Similarity metric learning"

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Griffin, Andrew, Sean Griffin, Kristofer Lasko, Megan Maloney, S. Blundell, Michael Collins, and Nicole Wayant. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40182.

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Анотація:
Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.
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