Name
Title | ||
|---|---|---|
A Minimax Framework for Classification with Applications to Images and High Dimensional Data |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TPAMI.2014.2327978 | IEEE Trans. Pattern Anal. Mach. Intell. |
Keywords | Field | DocType |
kernel,image processing,signal-dependent distortions,decision making,regression analysis,pattern classification,multiclass classification,fitting errors,minimax optimization,generalized multiplicative distortion,bayesian optimal decision,optimal decision rules,lasso regression,elastic net regression,support vector machine,regression models,kernel techniques,ridge regression,gene expressions,high dimensional data,generalized multiplicative distortions,minimax classification framework,images,support vector machines,manifolds,uncertainty,face recognition,nonlinear distortion | Structured support vector machine,Data mining,Minimax,Pattern recognition,Computer science,Elastic net regularization,Categorical variable,Lasso (statistics),Support vector machine,Artificial intelligence,Relevance vector machine,Multiclass classification | Journal |
Volume | Issue | ISSN |
36 | 11 | 0162-8828 |
Citations | PageRank | References |
11 | 0.51 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Cheng | 1 | 40 | 7.06 |
| Hongbo Zhou | 2 | 57 | 2.92 |
| Jie Cheng | 3 | 95 | 5.77 |
| Huiqing Li | 4 | 34 | 1.21 |