297 citations · 450 across the 15 of their papers we have counts for
15 papers
Learning using granularity statistical invariants for classification
Ting-Ting Zhu, Yuan-Hai Shao, Chun-Na Li +1
Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems. Ho…
Generalization-Memorization Machines
Zhen Wang, Yuan-Hai Shao
Classifying the training data correctly without over-fitting is one of the goals in machine learning. In this paper, we propose a generalization-memorization mechanism, including a…
Nonlinear Kernel Support Vector Machine with 0-1 Soft Margin Loss
Ju Liu, Ling-Wei Huang, Yuan-Hai Shao +2
Recent advance on linear support vector machine with the 0-1 soft margin loss (-SVM) shows that the 0-1 loss problem can be solved directly. However, its theoretical and a…
Comprehensive Review On Twin Support Vector Machines
M. Tanveer, T. Rajani, R. Rastogi +2
Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classific…
Fuzzy Discriminant Clustering with Fuzzy Pairwise Constraints
Zhen Wang, Shan-Shan Wang, Lan Bai +2
In semi-supervised fuzzy clustering, this paper extends the traditional pairwise constraint (i.e., must-link or cannot-link) to fuzzy pairwise constraint. The fuzzy pairwise constr…
Two-dimensional Bhattacharyya bound linear discriminant analysis with its applications
Yan-Ru Guo, Yan-Qin Bai, Chun-Na Li +2
Recently proposed L2-norm linear discriminant analysis criterion via the Bhattacharyya error bound estimation (L2BLDA) is an effective improvement of linear discriminant analysis (…
Capped norm linear discriminant analysis and its applications
Jiakou Liu, Xiong Xiong, Pei-Wei Ren +3
Classical linear discriminant analysis (LDA) is based on squared Frobenious norm and hence is sensitive to outliers and noise. To improve the robustness of LDA, in this paper, we i…
Principal Component Analysis Based on T-norm Maximization
Xiang-Fei Yang, Yuan-Hai Shao, Chun-Na Li +2
Classical principal component analysis (PCA) may suffer from the sensitivity to outliers and noise. Therefore PCA based on -norm and -norm () have been s…
Multiple Flat Projections for Cross-manifold Clustering
Lan Bai, Yuan-Hai Shao, Wei-Jie Chen +2
Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projectio…
Single and Union Non-parallel Support Vector Machine Frameworks
Chun-Na Li, Yuan-Hai Shao, Huajun Wang +4
Considering the classification problem, we summarize the nonparallel support vector machines with the nonparallel hyperplanes to two types of frameworks. The first type constructs…
A general model for plane-based clustering with loss function
Zhen Wang, Yuan-Hai Shao, Lan Bai +2
In this paper, we propose a general model for plane-based clustering. The general model contains many existing plane-based clustering methods, e.g., k-plane clustering (kPC), proxi…
Ramp-based Twin Support Vector Clustering
Zhen Wang, Xu Chen, Chun-Na Li +1
Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impa…
Robust Bhattacharyya bound linear discriminant analysis through adaptive algorithm
Chun-Na Li, Yuan-Hai Shao, Zhen Wang +1
In this paper, we propose a novel linear discriminant analysis criterion via the Bhattacharyya error bound estimation based on a novel L1-norm (L1BLDA) and L2-norm (L2BLDA). Both L…
Generalized two-dimensional linear discriminant analysis with regularization
Chun-Na Li, Yuan-Hai Shao, Wei-Jie Chen +2
Recent advances show that two-dimensional linear discriminant analysis (2DLDA) is a successful matrix based dimensionality reduction method. However, 2DLDA may encounter the singul…
Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems
Zhen Wang, Yuan-Hai Shao, Lan Bai +2
Stochastic gradient descent algorithm has been successfully applied on support vector machines (called PEGASOS) for many classification problems. In this paper, stochastic gradient…