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Journal articles on the topic 'Unsupervised categorization'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Pothos, Emmanuel M., and Nick Chater. "Unsupervised Categorization and Category Learning." Quarterly Journal of Experimental Psychology Section A 58, no. 4 (May 2005): 733–52. http://dx.doi.org/10.1080/02724980443000322.

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When people categorize a set of items in a certain way they often change their perceptions for these items so that they become more compatible with the learned categorization. In two experiments we examined whether such changes are extensive enough to change the unsupervised categorization for the items—that is, the categorization of the items that is considered more intuitive or natural without any learning. In Experiment 1 we directly employed an unsupervised categorization task; in Experiment 2 we collected similarity ratings for the items and inferred unsupervised categorizations using Pot

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2

Heidemann, Gunther. "Unsupervised image categorization." Image and Vision Computing 23, no. 10 (September 2005): 861–76. http://dx.doi.org/10.1016/j.imavis.2005.05.016.

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3

Clapper, John P., and Gordon H. Bower. "Adaptive categorization in unsupervised learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 28, no. 5 (September 2002): 908–23. http://dx.doi.org/10.1037/0278-7393.28.5.908.

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4

Wang, Xiaozhe, Liang Wang, Anthony Wirth, and Leonardo Lopes. "Unsupervised categorization of human motion sequences." Intelligent Data Analysis 17, no. 6 (November 6, 2013): 1057–74. http://dx.doi.org/10.3233/ida-130620.

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5

Yuchi Huang, Qingshan Liu, Fengjun Lv, Yihong Gong, and Dimitris N. Metaxas. "Unsupervised Image Categorization by Hypergraph Partition." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 6 (June 2011): 1266–73. http://dx.doi.org/10.1109/tpami.2011.25.

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6

Dolgikh, Serge. "Categorization in Unsupervised Generative Selflearning Systems." International Journal of Modern Education and Computer Science 13, no. 3 (June 8, 2021): 68–78. http://dx.doi.org/10.5815/ijmecs.2021.03.06.

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Gliozzo, Alfio, Carlo Strapparava, and Ido Dagan. "Improving text categorization bootstrapping via unsupervised learning." ACM Transactions on Speech and Language Processing 6, no. 1 (October 2009): 1–24. http://dx.doi.org/10.1145/1596515.1596516.

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8

Pothos, Emmanuel M., and Nick Chater. "A simplicity principle in unsupervised human categorization." Cognitive Science 26, no. 3 (May 2002): 303–43. http://dx.doi.org/10.1207/s15516709cog2603_6.

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9

YANG, SHICAI, GEORGE BEBIS, MUHAMMAD HUSSAIN, GHULAM MUHAMMAD, and ANWAR M. MIRZA. "UNSUPERVISED DISCOVERY OF VISUAL FACE CATEGORIES." International Journal on Artificial Intelligence Tools 22, no. 01 (February 2013): 1250029. http://dx.doi.org/10.1142/s0218213012500297.

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Human faces can be arranged into different face categories using information from common visual cues such as gender, ethnicity, and age. It has been demonstrated that using face categorization as a precursor step to face recognition improves recognition rates and leads to more graceful errors. Although face categorization using common visual cues yields meaningful face categories, developing accurate and robust gender, ethnicity, and age categorizers is a challenging issue. Moreover, it limits the overall number of possible face categories and, in practice, yields unbalanced face categories wh

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Ell, Shawn W., and F. Gregory Ashby. "The impact of category separation on unsupervised categorization." Attention, Perception, & Psychophysics 74, no. 2 (November 9, 2011): 466–75. http://dx.doi.org/10.3758/s13414-011-0238-z.

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Begin, J., and R. Proulx. "Categorization in unsupervised neural networks: the Eidos model." IEEE Transactions on Neural Networks 7, no. 1 (January 1996): 147–54. http://dx.doi.org/10.1109/72.478399.

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Ashby, F. Gregory, Sarah Queller, and Patricia M. Berretty. "On the dominance of unidimensional rules in unsupervised categorization." Perception & Psychophysics 61, no. 6 (August 1999): 1178–99. http://dx.doi.org/10.3758/bf03207622.

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13

Yan, Xiaoqiang, Yangdong Ye, and Zhengzheng Lou. "Unsupervised video categorization based on multivariate information bottleneck method." Knowledge-Based Systems 84 (August 2015): 34–45. http://dx.doi.org/10.1016/j.knosys.2015.03.028.

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14

Yan, Xiaoqiang, Yangdong Ye, Xueying Qiu, Milos Manic, and Hui Yu. "CMIB: Unsupervised Image Object Categorization in Multiple Visual Contexts." IEEE Transactions on Industrial Informatics 16, no. 6 (June 2020): 3974–86. http://dx.doi.org/10.1109/tii.2019.2939278.

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15

Cui, Peng, Fei Wang, Li-Feng Sun, Jian-Wei Zhang, and Shi-Qiang Yang. "A Matrix-Based Approach to Unsupervised Human Action Categorization." IEEE Transactions on Multimedia 14, no. 1 (February 2012): 102–10. http://dx.doi.org/10.1109/tmm.2011.2176110.

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16

Edwards, Darren J. "Unsupervised categorization with a child sample: category cohesion development." European Journal of Developmental Psychology 14, no. 1 (March 23, 2016): 75–86. http://dx.doi.org/10.1080/17405629.2016.1158706.

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Hossain, Md Shafayat, Ahmedullah Aziz, and Mohammad Wahidur Rahman. "Unsupervised Object Matching and Categorization via Agglomerative Correspondence Clustering." Signal & Image Processing : An International Journal 4, no. 1 (February 28, 2013): 35–47. http://dx.doi.org/10.5121/sipij.2013.4103.

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18

Wang, Peng, Zhi-Qiang Liu, and Shi-Qiang Yang. "Investigation on unsupervised clustering algorithms for video shot categorization." Soft Computing 11, no. 4 (August 8, 2006): 355–60. http://dx.doi.org/10.1007/s00500-006-0089-z.

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19

Limsettho, Nachai, Hideaki Hata, Akito Monden, and Kenichi Matsumoto. "Unsupervised Bug Report Categorization Using Clustering and Labeling Algorithm." International Journal of Software Engineering and Knowledge Engineering 26, no. 07 (September 2016): 1027–53. http://dx.doi.org/10.1142/s0218194016500352.

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Bug reports are one of the most crucial information sources for software engineering offering answers to many questions. Yet, getting these answers is not always easy; the information in bug reports is often implicit and some processes are required to extract the meaning of these reports. Most research in this area employ a supervised learning approach to classify bug reports so that required types of reports could be identified. However, this approach often requires an immense amount of time and effort, the resources that already too scarce in many projects. We aim to develop an automated fra

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20

COLREAVY, E., and S. LEWANDOWSKY. "Strategy development and learning differences in supervised and unsupervised categorization." Memory & Cognition 36, no. 4 (June 1, 2008): 762–75. http://dx.doi.org/10.3758/mc.36.4.762.

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Pothos, Emmanuel M., Darren J. Edwards, and Amotz Perlman. "Supervised versus Unsupervised Categorization: Two Sides of the Same Coin?" Quarterly Journal of Experimental Psychology 64, no. 9 (September 2011): 1692–713. http://dx.doi.org/10.1080/17470218.2011.554990.

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22

Wang, Shiping, Jinyu Cai, Qihao Lin, and Wenzhong Guo. "An Overview of Unsupervised Deep Feature Representation for Text Categorization." IEEE Transactions on Computational Social Systems 6, no. 3 (June 2019): 504–17. http://dx.doi.org/10.1109/tcss.2019.2910599.

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23

JUPP, JULIE, and JOHN S. GERO. "Visual style: Qualitative and context-dependent categorization." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 20, no. 3 (June 27, 2006): 247–66. http://dx.doi.org/10.1017/s0890060406060197.

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Style is an ordering principle by which to structure artifacts in a design domain. The application of a visual order entails some explicit grouping property that is both cognitively plausible and contextually dependent. Central to cognitive–contextual notions are the type of representation used in analysis and the flexibility to allow semantic interpretation. We present a model of visual style based on the concept of similarity as a qualitative context-dependent categorization. The two core components of the model are semantic feature extraction and self-organizing maps (SOMs). The model propo

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24

Xia, Rong Ze, Yan Jia, and Hu Li. "A Text Categorization Method Based on SVM and Improved K-Means." Applied Mechanics and Materials 427-429 (September 2013): 2449–53. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2449.

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Traditional supervised classification method such as support vector machine (SVM) could achieve high performance in text categorization. However, we should first hand-labeled the samples before classifying. Its a time-consuming task. Unsupervised method such as k-means could also be used for handling the text categorization problem. However, Traditional k-means could easily be affected by several isolated observations. In this paper, we proposed a new text categorization method. First we improved the traditional k-means clustering algorithm. The improved k-means is used for clustering vectors

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25

Edwards, Darren J., Amotz Perlman, and Phil Reed. "Unsupervised Categorization in a sample of children with autism spectrum disorders." Research in Developmental Disabilities 33, no. 4 (July 2012): 1264–69. http://dx.doi.org/10.1016/j.ridd.2012.02.021.

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26

Xu, Tao, and Qinke Peng. "Extended information inference model for unsupervised categorization of web short texts." Journal of Information Science 38, no. 6 (October 15, 2012): 512–31. http://dx.doi.org/10.1177/0165551512448985.

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27

Lee, Chung-Hong, and Hsin-Chang Yang. "Construction of supervised and unsupervised learning systems for multilingual text categorization." Expert Systems with Applications 36, no. 2 (March 2009): 2400–2410. http://dx.doi.org/10.1016/j.eswa.2007.12.052.

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28

Lozano, M. A., F. Escolano, B. Bonev, P. Suau, W. Aguilar, J. M. Saez, and M. A. Cazorla. "Region and constellations based categorization of images with unsupervised graph learning." Image and Vision Computing 27, no. 7 (June 2009): 960–78. http://dx.doi.org/10.1016/j.imavis.2008.09.011.

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29

Iwata, Tomoharu, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, and Joshua B. Tenenbaum. "Parametric Embedding for Class Visualization." Neural Computation 19, no. 9 (September 2007): 2536–56. http://dx.doi.org/10.1162/neco.2007.19.9.2536.

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We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised,

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30

Löw, Andreas, Shlomo Bentin, Brigitte Rockstroh, Yaron Silberman, Annette Gomolla, Rudolf Cohen, and Thomas Elbert. "Semantic Categorization in the Human Brain." Psychological Science 14, no. 4 (July 2003): 367–72. http://dx.doi.org/10.1111/1467-9280.24451.

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We examined the cortical representation of semantic categorization using magnetic source imaging in a task that revealed both dissociations among superordinate categories and associations among different base-level concepts within these categories. Around 200 ms after stimulus onset, the spatiotemporal correlation of brain activity elicited by base-level concepts was greater within than across superordinate categories in the right temporal lobe. Unsupervised clustering of data showed similar categorization between 210 and 450 ms mainly in the left hemisphere. This pattern suggests that well-de

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31

Ciocca, Gianluigi, Claudio Cusano, Simone Santini, and Raimondo Schettini. "On the use of supervised features for unsupervised image categorization: An evaluation." Computer Vision and Image Understanding 122 (May 2014): 155–71. http://dx.doi.org/10.1016/j.cviu.2014.01.010.

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32

Robinson, Saul. "Multi-Label Classification of Contributing Causal Factors in Self-Reported Safety Narratives." Safety 4, no. 3 (July 20, 2018): 30. http://dx.doi.org/10.3390/safety4030030.

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Three methods are demonstrated for automated classification of aviation safety narratives within an existing complex taxonomy. Utilizing latent semantic analysis trained against 4497 narratives at the sentence level, primary problem and contributing factor labels were assessed. Results from a sample of 2987 narratives provided a mean unsupervised categorization precision of 0.35% and recall of 0.78% for contributing-factors within the taxonomy. Categorization of the primary problem at the sentence level resulted in a modal accuracy of 0.46%. Overall, the results suggested that the demonstrated

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33

Arya, Vaishali, and Rashmi Agrawal. "Improvement in Text Categorization Using Semi-Supervised Approach and Lexical Chains." Journal of Computational and Theoretical Nanoscience 16, no. 12 (December 1, 2019): 5122–26. http://dx.doi.org/10.1166/jctn.2019.8573.

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Text categorization is used for assigning the class labels to the available data set or providing a conceptual view to a data set. The text categorization can be performed in two ways supervised way, and in an unsupervised way. But alone neither can perform well in the categorization of data set. So a semi-supervised model with the combination of lexical chains is used to perform the task of categorization. In the proposed semi-supervised model the lexical chains are used to determine the numbers of clusters has to be formed using k-means clustering. This ‘k-means’ will divide the data set int

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34

Soto, P. J., J. D. Bermudez, P. N. Happ, and R. Q. Feitosa. "A COMPARATIVE ANALYSIS OF UNSUPERVISED AND SEMI-SUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CATEGORIZATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 167–73. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-167-2019.

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<p><strong>Abstract.</strong> This work aims at investigating unsupervised and semi-supervised representation learning methods based on generative adversarial networks for remote sensing scene classification. The work introduces a novel approach, which consists in a semi-supervised extension of a prior unsupervised method, known as MARTA-GAN. The proposed approach was compared experimentally with two baselines upon two public datasets, <i>UC-MERCED</i> and <i>NWPU-RESISC45</i>. The experiments assessed the performance of each approach under different a

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35

Yan, Xiaoqiang, Zhengzheng Lou, Shizhe Hu, and Yangdong Ye. "Multi-task Information Bottleneck Co-clustering for Unsupervised Cross-view Human Action Categorization." ACM Transactions on Knowledge Discovery from Data 14, no. 2 (March 7, 2020): 1–23. http://dx.doi.org/10.1145/3375394.

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36

Song, Wei, Lim Cheon Choi, Soon Cheol Park, and Xiao Feng Ding. "Fuzzy evolutionary optimization modeling and its applications to unsupervised categorization and extractive summarization." Expert Systems with Applications 38, no. 8 (August 2011): 9112–21. http://dx.doi.org/10.1016/j.eswa.2010.12.102.

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37

Jiang, Zigui, Rongheng Lin, and Fangchun Yang. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data." Energies 11, no. 9 (August 26, 2018): 2235. http://dx.doi.org/10.3390/en11092235.

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Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm

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38

Guo, Lin, Wanli Zuo, Tao Peng, and Lin Yue. "Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/723469.

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The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the p

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39

Edwards, Darren J., and Rodger Wood. "Unsupervised categorization with individuals diagnosed as having moderate traumatic brain injury: Over-selective responding." Brain Injury 30, no. 13-14 (September 14, 2016): 1576–80. http://dx.doi.org/10.1080/02699052.2016.1199899.

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40

Dong, Yuan, Nan Zhao, Shiguo Lian, Shusheng Cen, and Wei Liu. "Unsupervised mining of visually consistent shots for sports genre categorization over large-scale database." Telecommunication Systems 59, no. 3 (December 12, 2014): 381–91. http://dx.doi.org/10.1007/s11235-014-9943-y.

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41

Reza, Nosheen, William Bone, Pankhuri Singhal, Anurag Verma, Ashwin C. Murthy, Srinivas Denduluri, Srinath Adusumalli, Macrylyn D. Ritchie, and Thomas P. Cappola. "42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure." Journal of Clinical and Translational Science 5, s1 (March 2021): 46–47. http://dx.doi.org/10.1017/cts.2021.524.

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ABSTRACT IMPACT: Measuring and analyzing qualitative and quantitative traits using phenomics approaches will yield previously unrecognized heart failure subphenotypes and has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure. OBJECTIVES/GOALS: Current classification schemes fail to capture the broader pathophysiologic heterogeneity in heart failure. Phenomics offers a newer unbiased approach to identify subtypes of complex disease syndromes, like heart failure. The

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42

Kouzoupis, Spyros, Andreas Neocleous, and Irene Athanassakis. "Categorization of Mouse Ultrasonic Vocalizations Using Machine Learning Techniques." Acoustics 1, no. 4 (November 4, 2019): 837–46. http://dx.doi.org/10.3390/acoustics1040050.

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A study of the ultrasonic vocalizations of several adult male BALB/c mice in the presence of a female, is undertaken in this study. A total of 179 distinct ultrasonic syllables referred to as “phonemes” are isolated, and in the resulting dataset, k-means and agglomerative clustering algorithms are implemented to group the ultrasonic vocalizations into clusters based on features extracted from their pitch contours. In order to find the optimal number of clusters, the elbow method was used, and nine distinct categories were obtained. Results when the k-means method was applied are presented thro

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43

Love, Bradley C. "Environment and Goals Jointly Direct Category Acquisition." Current Directions in Psychological Science 14, no. 4 (August 2005): 195–99. http://dx.doi.org/10.1111/j.0963-7214.2005.00363.x.

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Developing categorization schemes involves discovering structures in the world that support a learner's goals. Existing models of category learning, such as exemplar and prototype models, neglect the role of goals in shaping conceptual organization. Here, a clustering approach is discussed that reflects the joint influences of the environment and goals in directing category acquisition. Clusters are a flexible representational medium that exhibits properties of exemplar, prototype, and rule-based models. Clusters reflect the natural bundles of correlated features present in our environment. Th

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44

Thomas, Elizabeth, Marc M. Van Hulle, and Rufin Vogel. "Encoding of Categories by Noncategory-Specific Neurons in the Inferior Temporal Cortex." Journal of Cognitive Neuroscience 13, no. 2 (February 1, 2001): 190–200. http://dx.doi.org/10.1162/089892901564252.

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In order to understand how the brain codes natural categories, e.g., trees and fish, recordings were made in the anterior part of the macaque inferior temporal (IT) cortex while the animal was performing a tree/nontree categorization task. Most single cells responded to exemplars of more than one category while other neurons responded only to a restricted set of exemplars of a given category. Since it is still not known which type of cells contribute and what is the nature of the code used for categorization in IT, we have performed an analysis on single-cell data. A Kohonen self-organizing ma

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45

Toldo, Marco, Andrea Maracani, Umberto Michieli, and Pietro Zanuttigh. "Unsupervised Domain Adaptation in Semantic Segmentation: A Review." Technologies 8, no. 2 (June 21, 2020): 35. http://dx.doi.org/10.3390/technologies8020035.

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The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attracting a wide interest since semantic segmentation models require a huge amount of labeled data and the lack of data fitting specific requirements is the main limitation in the deployment of these techniques. This field has been recently explored and has rapidly grown with a large number of ad-hoc approaches. This motivates us to build a comprehensive overview of the proposed methodologies and to provide a clear categor

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Gourisaria, Mahendra Kumar, Harshvardhan GM, Rakshit Agrawal, Sudhansu Shekhar Patra, Siddharth Swarup Rautaray, and Manjusha Pandey. "Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals." International Journal of E-Health and Medical Communications 12, no. 6 (November 2021): 1–24. http://dx.doi.org/10.4018/ijehmc.20211101.oa9.

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Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple

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47

Baraldi, Andrea, and Flavio Parmiggiani. "A neural network for unsupervised categorization of multivalued input patterns: an application to satellite imaee clustering." IEEE Transactions on Geoscience and Remote Sensing 33, no. 2 (March 1995): 305–16. http://dx.doi.org/10.1109/tgrs.1995.8746011.

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48

Baraldi, A., and F. Parmiggiani. "A neural network for unsupervised categorization of multivalued input patterns: an application to satellite image clustering." IEEE Transactions on Geoscience and Remote Sensing 33, no. 2 (March 1995): 305–16. http://dx.doi.org/10.1109/36.377930.

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Jha, Ashutosh, and Debashis Saha. "Examining categorization of Telecom Circles in India using unsupervised k-means clustering on techno-economic indicators." DECISION 46, no. 4 (November 12, 2019): 365–83. http://dx.doi.org/10.1007/s40622-019-00225-6.

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50

Aditya Prakash. "Twitter Sentimental Analysis." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 18, 2020): 355–59. http://dx.doi.org/10.46501/ijmtst061266.

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Twitter sentiment analysis (TSA) provides the methods to survey public emotions about the products or events associated with them. Categorization of opinions through tweets involves a great scope of study and may yield interesting results and insights on public opinion and social behavior towards different events, services, product, geopolitical issues, situations and scenarios that concern mankind at large. These attributes are expressed explicitly through emoticons, exclamation, sentiment words and so on. In this paper, we introduce a word embedding (Word2Vec) technique obtained by unsupervi

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