• Publications
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Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization
TLDR
Feature selection, the process of selecting a subset of relevant features, is a key component in building robust machine learning models for classification, clustering and other tasks. Expand
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
TLDR
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. Expand
The Constrained Laplacian Rank Algorithm for Graph-Based Clustering
TLDR
We propose a novel graph-based clustering model that learns a graph with exactly k connected components (where k is the number of clusters) and derive optimization algorithms to solve them. Expand
Clustering and projected clustering with adaptive neighbors
TLDR
We propose a novel clustering model to learn the data similarity matrix and clustering structure simultaneously to achieve the optimal clustering results. Expand
Discriminative Least Squares Regression for Multiclass Classification and Feature Selection
TLDR
We present a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. Expand
Multi-View K-Means Clustering on Big Data
TLDR
We propose a new robust large-scale multi-view clustering method to integrate heterogeneous representations of largescale data. Expand
Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection
TLDR
In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding Learning and sparse regressions are jointly performed. Expand
Learning a Mahalanobis distance metric for data clustering and classification
TLDR
Distance metric is a key issue in many machine learning algorithms. Expand
Image Clustering Using Local Discriminant Models and Global Integration
TLDR
We propose a new image clustering algorithm, referred to as clustering using local discriminant models and global integration (LDMGI). Expand
Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours
TLDR
We propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously. Expand
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