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Low-Rank Matrix Approximation with Manifold Regularization

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2 Author(s)
Zhenyue Zhang ; Zhejiang University, Hangzhou ; Keke Zhao

This paper proposes a new model of low-rank matrix factorization that incorporates manifold regularization to the matrix factorization. Superior to the graph-regularized nonnegative matrix factorization, this new regularization model has globally optimal and closed-form solutions. A direct algorithm (for data with small number of points) and an alternate iterative algorithm with inexact inner iteration (for large scale data) are proposed to solve the new model. A convergence analysis establishes the global convergence of the iterative algorithm. The efficiency and precision of the algorithm are demonstrated numerically through applications to six real-world datasets on clustering and classification. Performance comparison with existing algorithms shows the effectiveness of the proposed method for low-rank factorization in general.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:35 ,  Issue: 7 )