Дисертації з теми "Structured sparsity model"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-15 дисертацій для дослідження на тему "Structured sparsity model".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте дисертації для різних дисциплін та оформлюйте правильно вашу бібліографію.
Tillander, Annika. "Classification models for high-dimensional data with sparsity patterns." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-95664.
Повний текст джерелаMed dagens teknik, till exempel spektrometer och genchips, alstras data i stora mängder. Detta överflöd av data är inte bara till fördel utan orsakar även vissa problem, vanligtvis är antalet variabler (p) betydligt fler än antalet observation (n). Detta ger så kallat högdimensionella data vilket kräver nya statistiska metoder, då de traditionella metoderna är utvecklade för den omvända situationen (p<n). Dessutom är det vanligtvis väldigt få av alla dessa variabler som är relevanta för något givet projekt och styrkan på informationen hos de relevanta variablerna är ofta svag. Därav brukar denna typ av data benämnas som gles och svag (sparse and weak). Vanligtvis brukar identifiering av de relevanta variablerna liknas vid att hitta en nål i en höstack. Denna avhandling tar upp tre olika sätt att klassificera i denna typ av högdimensionella data. Där klassificera innebär, att genom ha tillgång till ett dataset med både förklaringsvariabler och en utfallsvariabel, lära en funktion eller algoritm hur den skall kunna förutspå utfallsvariabeln baserat på endast förklaringsvariablerna. Den typ av riktiga data som används i avhandlingen är microarrays, det är cellprov som visar aktivitet hos generna i cellen. Målet med klassificeringen är att med hjälp av variationen i aktivitet hos de tusentals gener (förklaringsvariablerna) avgöra huruvida cellprovet kommer från cancervävnad eller normalvävnad (utfallsvariabeln). Det finns klassificeringsmetoder som kan hantera högdimensionella data men dessa är ofta beräkningsintensiva, därav fungera de ofta bättre för diskreta data. Genom att transformera kontinuerliga variabler till diskreta (diskretisera) kan beräkningstiden reduceras och göra klassificeringen mer effektiv. I avhandlingen studeras huruvida av diskretisering påverkar klassificeringens prediceringsnoggrannhet och en mycket effektiv diskretiseringsmetod för högdimensionella data föreslås. Linjära klassificeringsmetoder har fördelen att vara stabila. Nackdelen är att de kräver en inverterbar kovariansmatris och vilket kovariansmatrisen inte är för högdimensionella data. I avhandlingen föreslås ett sätt att skatta inversen för glesa kovariansmatriser med blockdiagonalmatris. Denna matris har dessutom fördelen att det leder till additiv klassificering vilket möjliggör att välja hela block av relevanta variabler. I avhandlingen presenteras även en metod för att identifiera och välja ut blocken. Det finns också probabilistiska klassificeringsmetoder som har fördelen att ge sannolikheten att tillhöra vardera av de möjliga utfallen för en observation, inte som de flesta andra klassificeringsmetoder som bara predicerar utfallet. I avhandlingen förslås en sådan Bayesiansk metod, givet den blockdiagonala matrisen och normalfördelade utfallsklasser. De i avhandlingen förslagna metodernas relevans och fördelar är visade genom att tillämpa dem på simulerade och riktiga högdimensionella data.
Vinyes, Marina. "Convex matrix sparsity for demixing with an application to graphical model structure estimation." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1130/document.
Повний текст джерелаThe goal of machine learning is to learn a model from some data that will make accurate predictions on data that it has not seen before. In order to obtain a model that will generalize on new data, and avoid overfitting, we need to restrain the model. These restrictions are usually some a priori knowledge of the structure of the model. First considered approaches included a regularization, first ridge regression and later Lasso regularization for inducing sparsity in the solution. Sparsity, also known as parsimony, has emerged as a fundamental concept in machine learning. Parsimonious models are appealing since they provide more interpretability and better generalization (avoid overfitting) through the reduced number of parameters. Beyond general sparsity and in many cases, models are constrained structurally so they have a simple representation in terms of some fundamental elements, consisting for example of a collection of specific vectors, matrices or tensors. These fundamental elements are called atoms. In this context, atomic norms provide a general framework for estimating these sorts of models. The goal of this thesis is to use the framework of convex sparsity provided by atomic norms to study a form of matrix sparsity. First, we develop an efficient algorithm based on Frank-Wolfe methods that is particularly adapted to solve problems with an atomic norm regularization. Then, we focus on the structure estimation of Gaussian graphical models, where the structure of the graph is encoded in the precision matrix and study the case with unobserved variables. We propose a convex formulation with an algorithmic approach and provide a theoretical result that states necessary conditions for recovering the desired structure. Finally, we consider the problem of signal demixing into two or more components via the minimization of a sum of norms or gauges, encoding each a structural prior on the corresponding components to recover. In particular, we provide general exact recovery guarantees in the noiseless setting based on incoherence measures
Smith, Chandler B. "Sparsity Constrained Inverse Problems - Application to Vibration-based Structural Health Monitoring." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1143.
Повний текст джерелаKim, Yookyung. "Compressed Sensing Reconstruction Using Structural Dependency Models." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/238613.
Повний текст джерелаMcGrady, Christopher Dwain. "Linking Rheological and Processing Behavior to Molecular Structure in Sparsely-Branched Polyethylenes Using Constitutive Relationships." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/37924.
Повний текст джерелаPh. D.
Yan, Enxu. "Sublinear-Time Learning and Inference for High-Dimensional Models." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1207.
Повний текст джерелаRösmann, Christoph [Verfasser], Torsten [Akademischer Betreuer] Bertram, and Martin [Gutachter] Mönnigmann. "Time-optimal nonlinear model predictive control : Direct transcription methods with variable discretization and structural sparsity exploitation / Christoph Rösmann ; Gutachter: Martin Mönnigmann ; Betreuer: Torsten Bertram." Dortmund : Universitätsbibliothek Dortmund, 2019. http://d-nb.info/1199106364/34.
Повний текст джерелаRoulet, Vincent. "On the geometry of optimization problems and their structure." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE069/document.
Повний текст джерелаIn numerous fields such as machine learning, operational research or circuit design, a task is modeled by a set of parameters to be optimized in order to take the best possible decision. Formally, the problem amounts to minimize a function describing the desired objective with iterative algorithms. The development of these latter depends then on the characterization of the geometry of the function or the structure of the problem. In a first part, this thesis studies how sharpness of a function around its minimizers can be exploited by restarting classical algorithms. Optimal schemes are presented for general convex problems. They require however a complete description of the function that is rarely available. Adaptive strategies are therefore developed and shown to achieve nearly optimal rates. A specific analysis is then carried out for sparse problems that seek for compressed representation of the variables of the problem. Their underlying conic geometry, that describes sharpness of the objective, is shown to control both the statistical performance of the problem and the efficiency of dedicated optimization methods by a single quantity. A second part is dedicated to machine learning problems. These perform predictive analysis of data from large set of examples. A generic framework is presented to both solve the prediction problem and simplify it by grouping either features, samples or tasks. Systematic algorithmic approaches are developed by analyzing the geometry induced by partitions of the data. A theoretical analysis is then carried out for grouping features by analogy to sparse methods
Kolar, Mladen. "Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/229.
Повний текст джерелаTodeschini, Adrien. "Probabilistic and Bayesian nonparametric approaches for recommender systems and networks." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0237/document.
Повний текст джерелаWe propose two novel approaches for recommender systems and networks. In the first part, we first give an overview of recommender systems and concentrate on the low-rank approaches for matrix completion. Building on a probabilistic approach, we propose novel penalty functions on the singular values of the low-rank matrix. By exploiting a mixture model representation of this penalty, we show that a suitably chosen set of latent variables enables to derive an expectation-maximization algorithm to obtain a maximum a posteriori estimate of the completed low-rank matrix. The resulting algorithm is an iterative soft-thresholded algorithm which iteratively adapts the shrinkage coefficients associated to the singular values. The algorithm is simple to implement and can scale to large matrices. We provide numerical comparisons between our approach and recent alternatives showing the interest of the proposed approach for low-rank matrix completion. In the second part, we first introduce some background on Bayesian nonparametrics and in particular on completely random measures (CRMs) and their multivariate extension, the compound CRMs. We then propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process, and naturally generalizes existing probabilistic models with overlapping block-structure to the sparse regime. Our construction builds on vectors of CRMs, and has interpretable parameters, each node being assigned a vector representing its level of affiliation to some latent communities. We develop methods for simulating this class of random graphs, as well as to perform posterior inference. We show that the proposed approach can recover interpretable structure from two real-world networks and can handle graphs with thousands of nodes and tens of thousands of edges
Suwanwimolkul, Suwichaya. "Adaptive Markov Random Fields for Structured Compressive Sensing." Thesis, 2018. http://hdl.handle.net/2440/117806.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2018
Hwang, Sung Ju. "Discriminative object categorization with external semantic knowledge." 2013. http://hdl.handle.net/2152/21320.
Повний текст джерелаtext
Mayrink, Vinicius Diniz. "Factor Models to Describe Linear and Non-linear Structure in High Dimensional Gene Expression Data." Diss., 2011. http://hdl.handle.net/10161/3865.
Повний текст джерелаAn important problem in the analysis of gene expression data is the identification of groups of features that are coherently expressed. For example, one often wishes to know whether a group of genes, clustered because of correlation in one data set, is still highly co-expressed in another data set. For some microarray platforms there are many, relatively short, probes for each gene of interest. In this case, it is possible that a given probe is not measuring its targeted transcript, but rather a different gene with a similar region (called cross-hybridization). Similarly, the incorrect mapping of short nucleotide sequences to a target gene is a common issue related to the young technology producing RNA-Seq data. The expression pattern across samples is a valuable source of information, which can be used to address distinct problems through the application of factor models. Our first study is focused on the identification of the presence/absence status of a gene in a sample. We compare our factor model to state-of-the-art detection methods; the results suggest superior performance of the factor analysis for detecting transcripts. In the second study, we apply factor models to investigate gene modules (groups of coherently expressed genes). Variation in the number of copies of regions of the genome is a well known and important feature of most cancers. Copy number alteration is detected for a group of genes in breast cancer; our goal is to examine this abnormality in the same chromosomal region for other types of tumors (Ovarian, Lung and Brain). In the third application, the expression pattern related to RNA-Seq count data is evaluated through a factor model based on the Poisson distribution. Here, the presence/absence of coherent patterns is closely associated with the number of incorrect read mappings. The final study of this dissertation is dedicated to the analysis of multi-factor models with linear and non-linear structure of interactions between latent factors. The interaction terms can have important implications in the model; they represent relationships between genes which cannot be captured in an ordinary analysis.
Dissertation
Xiong, Xin. "Efficient Jacobian Determination by Structure-Revealing Automatic Differentiation." Thesis, 2014. http://hdl.handle.net/10012/8197.
Повний текст джерелаSadhu, Ayan. "Decentralized Ambient System Identification of Structures." Thesis, 2013. http://hdl.handle.net/10012/7538.
Повний текст джерела