DocumentCode :
34615
Title :
A Minimax Framework for Classification with Applications to Images and High Dimensional Data
Author :
Qiang Cheng ; Hongbo Zhou ; Jie Cheng ; Huiqing Li
Author_Institution :
Comput. Sci. Dept., Southern Illinois Univ. Carbondale (SIUC), Carbondale, IL, USA
Volume :
36
Issue :
11
fYear :
2014
fDate :
Nov. 1 2014
Firstpage :
2117
Lastpage :
2130
Abstract :
This paper introduces a minimax framework for multiclass classification, which is applicable to general data including, in particular, imagery and other types of high-dimensional data. The framework consists of estimating a representation model that minimizes the fitting errors under a class of distortions of interest to an application, and deriving subsequently categorical information based on the estimated model. A variety of commonly used regression models, including lasso, elastic net and ridge regression, can be regarded as special cases that correspond to specific classes of distortions. Optimal decision rules are derived for this classification framework. By using kernel techniques the framework can account for nonlinearity in the input space. To demonstrate the power of the framework we consider a class of signal-dependent distortions and build a new family of classifiers as new special cases. This family of new methods-minimax classification with generalized multiplicative distortions-often outperforms the state-of-the-art classification methods such as the support vector machine in accuracy. Extensive experimental results on images, gene expressions and other types of data verify the effectiveness of the proposed framework.
Keywords :
decision making; image processing; pattern classification; regression analysis; support vector machines; elastic net regression; fitting errors; gene expressions; generalized multiplicative distortions; high dimensional data; images; kernel techniques; lasso regression; minimax classification framework; multiclass classification; optimal decision rules; regression models; ridge regression; signal-dependent distortions; support vector machine; Face recognition; Kernel; Manifolds; Nonlinear distortion; Support vector machines; Training; Uncertainty; Bayesian optimal decision; Multiclass classification; generalized multiplicative distortion; high dimensional data; kernel; minimax optimization;
fLanguage :
English
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher :
ieee
ISSN :
0162-8828
Type :
jour
DOI :
10.1109/TPAMI.2014.2327978
Filename :
6824834
Link To Document :
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