Wei Zheng
Guangxi Normal University
16 Papers
23 Citations
Wei Zheng is an academic researcher from Guangxi Normal University. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 8, co-authored 14 publications. Previous affiliations of Wei Zheng include Massey University.
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Papers
Unsupervised feature selection by self-paced learning regularization
TL;DR: A self-paced regularization is added in the sparse feature selection model to reduce the impact of outliers for conducting feature selection and Experimental results show that the proposed method outperforms the comparison methods.
151
Dynamic graph learning for spectral feature selection
TL;DR: This paper uses the least square loss function and an ℓ2,1-norm regularization to remove the effect of noisy and redundancy features, and uses the resulting local correlations among the features to dynamically learn a graph matrix from a low-dimensional space of original data.
95
Spectral rotation for deep one-step clustering
TL;DR: A deep spectral clustering method which embeds four parts in a unified framework with the following advantages, and develops a two-task deep clustering model with linear activation functions to output effective clustering result.
83
Half-Quadratic Minimization for Unsupervised Feature Selection on Incomplete Data
TL;DR: This article investigates a new UFS method for conducting UFS on incomplete data sets and designs an alternative optimization strategy to optimize the proposed objective function as well as theoretically and experimentally prove the convergence of the proposed optimization strategy.
72
Self-paced Learning for K-means Clustering Algorithm
TL;DR: By analyzing the experimental results, the clustering algorithm proposed in this paper achieves better performance than the compare algorithms on the five real data sets.
54