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New Tight Relaxations of Rank Minimization for Multi-Task Learning
Wei Chang, Feiping Nie, Rong Wang +1
Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace. It means learning multiple tasks joi…
Non-Gradient Manifold Neural Network
Rui Zhang, Ziheng Jiao, Hongyuan Zhang +1
Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent and thus has a slow convergence. In addition, softmax, as a decision layer, may i…
Auto-weighted Multi-view Feature Selection with Graph Optimization
Qi Wang, Xu Jiang, Mulin Chen +1
In this paper, we focus on the unsupervised multi-view feature selection which tries to handle high dimensional data in the field of multi-view learning. Although some graph-based…
Enhanced Principal Component Analysis under A Collaborative-Robust Framework
Rui Zhang, Hongyuan Zhang, Xuelong Li
Principal component analysis (PCA) frequently suffers from the disturbance of outliers and thus a spectrum of robust extensions and variations of PCA have been developed. However,…
Sparse PCA via -Norm Regularization for Unsupervised Feature Selection
Zhengxin Li, Feiping Nie, Jintang Bian +1
In the field of data mining, how to deal with high-dimensional data is an inevitable problem. Unsupervised feature selection has attracted more and more attention because it does n…
Unsupervised Graph Embedding via Adaptive Graph Learning
Rui Zhang, Yunxing Zhang, Xuelong Li
Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structur…
Adaptive Graph Auto-Encoder for General Data Clustering
Xuelong Li, Hongyuan Zhang, Rui Zhang
Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. H…
Embedding Graph Auto-Encoder for Graph Clustering
Hongyuan Zhang, Rui Zhang, Xuelong Li
Graph clustering, aiming to partition nodes of a graph into various groups via an unsupervised approach, is an attractive topic in recent years. To improve the representative abili…
Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity
Jinglin Xu, Junwei Han, Mingliang Xu +2
Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. Among various clustering approaches, the family of K-Means algorithms gai…
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms
Feiping Nie, Zhanxuan Hu, Xiaoqian Wang +3
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace…
Intrinsic Weight Learning Approach for Multi-view Clustering
Feiping Nie, Jing Li, Xuelong Li
Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. G…
Learning Feature Sparse Principal Components
Lai Tian, Feiping Nie, Xuelong Li
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously. Existing optimization met…
TLR: Transfer Latent Representation for Unsupervised Domain Adaptation
Pan Xiao, Bo Du, Jia Wu +3
Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptatio…