Robust unsupervised feature selection by nonnegative sparse subspace learning

doi:10.1016/j.neucom.2019.01.015

Robust unsupervised feature selection by nonnegative sparse subspace learning

2019-03-01

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DOI：10.1016/j.neucom.2019.01.015

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Abstract

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Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l(2,1)-norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l(1) -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution. (C) 2019 Published by Elsevier B.V.

Keywords:

Subspace learning

Non-negative matrix factorization

Unsupervised feature selection

Robust unsupervised feature selection by nonnegative sparse subspace learning

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