Graph Regularized Double Log-determinant Minimization for Subspace Clustering

Yuang Deng

doi:10.63313/JCSFT.9008

Authors

Yuang Deng Qingdao University, Qingdao 266000, China Author

DOI:

https://doi.org/10.63313/JCSFT.9008

Keywords:

Subspace Clustering, Machine learning, Log-determinant

Abstract

Subspace clustering methods have been successful in various applications such as face clustering. Existing methods usually adopt the l_1 or l_2-based norms to minimize the ﬁtting error. However, the ﬁtting residual constructed with such norms usually has structural information since these norms are computed in an element-wisely independent way and all elements of the residual are treated equally. To maximumly minimize structural information in residual so as to maximumly retain such information in recovered data, in this paper we propose to minimize ﬁtting error of each example with log-determinant rank approximation. Extensive experiments verify the effectiveness of the proposed method.

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