Name
Abstract | ||
|---|---|---|
Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image data sets (MIDS) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis [34] and to our previously developed accelerated kernel feature analysis [20]. Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis, and then be processed for composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates efficient and an effective feature representation, and has yielded a better classification performance for the proposed composite kernel subspace using a simple pattern classifier. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TKDE.2012.110 | Knowledge and Data Engineering, IEEE Transactions |
Keywords | Field | DocType |
feature extraction,image classification,image reconstruction,medical diagnostic computing,medical image processing,principal component analysis,CAD,MIDS,PC-KFA,computer aided diagnosis,data-dependent kernel approach,feature extraction,high-dimensional feature space,kernel adaptations,linear subspace,medical image data sets,nonlinear features,nonlinearly transformed samples,principal component analysis,principal composite kernel feature analysis,unclassified patterns,variance condition,Principal component analysis,data-dependent kernel,manifold structures,nonlinear subspace | Radial basis function kernel,Pattern recognition,Computer science,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,String kernel,Kernel method,Variable kernel density estimation,Machine learning,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
25 | 8 | 1041-4347 |
Citations | PageRank | References |
10 | 0.73 | 25 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuichi Motai | 1 | 230 | 24.68 |
| Hiroyuki Yoshida | 2 | 69 | 17.19 |