Name
Abstract | ||
|---|---|---|
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 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.11.076 | Neurocomputing |
Keywords | Field | DocType |
Low-rank representation,Unsupervised feature selection,Subspace learning,Feature self-representation,K-means clustering | Feature vector,Semi-supervised learning,Dimensionality reduction,Pattern recognition,Feature selection,Feature (computer vision),Unsupervised learning,Artificial intelligence,Linear classifier,Mathematics,Machine learning,Feature learning | Journal |
Volume | Issue | ISSN |
253 | C | 0925-2312 |
Citations | PageRank | References |
1 | 0.35 | 30 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rongyao Hu | 1 | 243 | 14.01 |
| Jie Cao | 2 | 627 | 73.36 |
| Debo Cheng | 3 | 210 | 10.90 |
| Wei He | 4 | 124 | 6.44 |
| Yonghua Zhu | 5 | 216 | 12.38 |
| Qing Xie | 6 | 112 | 9.12 |
| Guoqiu Wen | 7 | 2 | 0.70 |