Multi-subspace representation and discovery

Authors:
Dijun Luo;Feiping Nie;Chris Ding;Heng Huang
Affiliations:
Department of Computer Science and Engineering, University of Texas, Arlington, Texas;Department of Computer Science and Engineering, University of Texas, Arlington, Texas;Department of Computer Science and Engineering, University of Texas, Arlington, Texas;Department of Computer Science and Engineering, University of Texas, Arlington, Texas
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Year:
2011

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Abstract

This paper presents the multi-subspace discovery problem and provides a theoretical solution which is guaranteed to recover the number of subspaces, the dimensions of each subspace, and the members of data points of each subspace simultaneously. We further propose a data representation model to handle noisy real world data. We develop a novel optimization approach to learn the presented model which is guaranteed to converge to global optimizers. As applications of our models, we first apply our solutions as preprocessing in a series of machine learning problems, including clustering, classification, and semisupervised learning. We found that our method automatically obtains robust data presentation which preserves the affine subspace structures of high dimensional data and generate more accurate results in the learning tasks. We also establish a robust standalone classifier which directly utilizes our sparse and low rank representation model. Experimental results indicate our methods improve the quality of data by preprocessing and the standalone classifier outperforms some state-of-the-art learning approaches.