A minimax probabilistic approach to feature transformation for multi-class data

Authors:
Zhaohong Deng;Shitong Wang;Fu-Lai Chung
Affiliations:
School of Digital Media, Jiangnan University, WuXi, JiangSu, China and Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;School of Digital Media, Jiangnan University, WuXi, JiangSu, China and Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China
Venue:
Applied Soft Computing
Year:
2013

Citing 22
Cited 0

Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Feature Extraction Based on Decision Boundaries

IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust minimax approach to classification

The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization

The Journal of Machine Learning Research
A Feature Selection Newton Method for Support Vector Machine Classification

Computational Optimization and Applications
The Minimum Error Minimax Probability Machine

The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Improved breast cancer prognosis through the combination of clinical and genetic markers

Bioinformatics
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis

The Journal of Machine Learning Research
Second-Order Cone Programming Relaxation of Sensor Network Localization

SIAM Journal on Optimization
A comparative study of Minimax Probability Machine-based approaches for face recognition

Pattern Recognition Letters
Letters: A biased minimax probability machine-based scheme for relevance feedback in image retrieval

Neurocomputing
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Intelligence: An Introduction

Computational Intelligence: An Introduction
Isometric projection

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Using Maximum Margin Criterion and Minimax Probability Machine for Document Classification

WKDD '10 Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining
Minimax probability machine for iris recognition

ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Manifold-Regularized minimax probability machine

PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Imbalanced learning with a biased minimax probability machine

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A common neural-network model for unsupervised exploratory data analysis and independent component analysis

IEEE Transactions on Neural Networks
Nonlinear kernel-based statistical pattern analysis

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Feature transformation (FT) for dimensionality reduction has been deeply studied in the past decades. While the unsupervised FT algorithms cannot effectively utilize the discriminant information between classes in classification tasks, existing supervised FT algorithms have not yet caught up with the advances in classifier design. In this paper, based on the idea of controlling the probability of correct classification of a future test point as big as possible in the transformed feature space, a new supervised FT method called minimax probabilistic feature transformation (MPFT) is proposed for multi-class dataset. The experimental results on the UCI benchmark datasets and the high dimensional cancer gene expression datasets demonstrate that the proposed feature transformation methods are superior or competitive to several classical FT methods.