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Self-representation dimensionality reduction for multi-model classification
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DOI：10.1016/j.neucom.2016.11.076.png)
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Abstract
En 中文
Feature selection remove the noisy irrelevant samples and select the subset of representative features, in general, from the high-dimensional space of data has been a fatal significant technique in computer vision and machine learning. Afterwards, motivated by the interpretable ability of feature selection patterns, beside, and the successful use of low-rank constraint in static and sparse learning in the field of machine learning. We present a novel feature selection model with unsupervised learning by using low rank regression on loss function, and a sparsity term plus K-means clustering method on regularization term during this article. In order to distinguish from those existing state-of-the-art attribute selection measures, the propose method have described as follows: (1) represent the every feature by other features (including itself) via utilize the corresponding loss function with a feature-level self-express way; (2) embed K-means to generate pseudo class label information for the attribute selection as an pseudo supervised method, because of the supervised learning usually have the better recognition results than unsupervised learning; (3) also use the low-rank constraint to feature selection which considers two aspects of information inherent in data. The low-rank constraint takes the correlation of response variables into account, while an l(2,p)-norm regularizer considers the correlation between feature vectors and their corresponding response variables. The extensive relevant results of experiment on three multi-model comparison data demonstrated that our new unsupervised feature selection pattern outperforms the related approaches. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Low-rank representation
Unsupervised feature selection
Subspace learning
Feature self-representation
K-means clustering
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