1 citations · 1 across the 8 of their papers we have counts for
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Sharper Error Bounds in Late Fusion Multi-view Clustering Using Eigenvalue Proportion
Liang Du, Henghui Jiang, Xiaodong Li +5
Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown p…
Unsupervised Feature Selection Algorithm Based on Graph Filtering and Self-representation
Yunhui Liang, Jianwen Gan, Yan Chen +2
Aiming at the problem that existing methods could not fully capture the intrinsic structure of data without considering the higher-order neighborhood information of the data, we pr…
Clustering ensemble algorithm with high-order consistency learning
Jianwen Gan, Yan Chen, Peng Zhou +1
Most of the research on clustering ensemble focuses on designing practical consistency learning algorithms.To solve the problems that the quality of base clusters varies and the lo…
Hierarchical Multiple Kernel K-Means Algorithm Based on Sparse Connectivity
Lei Wang, Liang Du, Peng Zhou
Multiple kernel learning (MKL) aims to find an optimal, consistent kernel function. In the hierarchical multiple kernel clustering (HMKC) algorithm, sample features are extracted l…
Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking
Yunhui Liang, Jianwen Gan, Yan Chen +2
High-dimensional data is commonly encountered in numerous data analysis tasks. Feature selection techniques aim to identify the most representative features from the original high-…
Symmetry Nonnegative Matrix Factorization Algorithm Based on Self-paced Learning
Lei Wang, Liang Du, Peng Zhou +1
A symmetric nonnegative matrix factorization algorithm based on self-paced learning was proposed to improve the clustering performance of the model. It could make the model better…
Multiple Kernel Clustering via Local Regression Integration
Liang Du, Xin Ren, Haiying Zhang +1
Multiple kernel methods less consider the intrinsic manifold structure of multiple kernel data and estimate the consensus kernel matrix with quadratic number of variables, which ma…
Unsupervised feature selection algorithm framework based on neighborhood interval disturbance fusion
Xiaolin Lv, Liang Du, Peng Zhou +1
Feature selection technology is a key technology of data dimensionality reduction. Becauseof the lack of label information of collected data samples, unsupervised feature selection…