Adaptive Structure Concept Factorization for Multiview Clustering
| Autor: | Yuange Xie, Kun Zhan, Haibo Wang, Jinhui Shi, Jing Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
QA75
business.industry Computer science Cognitive Neuroscience Pattern recognition 02 engineering and technology Normalized mutual information computer.software_genre Non-negative matrix factorization Correlation Matrix (mathematics) Arts and Humanities (miscellaneous) Factorization 020204 information systems 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Artificial Intelligence & Image Processing 020201 artificial intelligence & image processing Artificial intelligence business Cluster analysis computer Data integration |
| Zdroj: | Neural Computation. 30:1080-1103 |
| ISSN: | 1530-888X 0899-7667 |
| DOI: | 10.1162/neco_a_01055 |
| Popis: | © 2018 Massachusetts Institute of Technology. Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views.With the interview correlation, a concept factorization-based multiview clustering method is developed for data integration, and the adaptive method correlates the affinity weights of all views. This method differs from nonnegative matrix factorization- based clustering methods in that it can be applicable to data sets containing negative values. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with state-of-theart approaches in terms of accuracy, normalized mutual information, and purity. |
| Databáze: | OpenAIRE |
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