Name
Abstract | ||
|---|---|---|
In pattern classification, Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are commonly used to reduce the dimensionality of input feature space. However, there exist some problems such that how many eigen vectors are needed to be the most effective in the transformation map as well as the lack of optimal separability in low dimensional data. In this paper, we present a new distance-based separator representation to solve these problems. The representation frame structure keeps adjustment pertaining to the problem complexity, and its dimensionality corresponds to the number of classes. Experimental results show that the new representation outperforms the PCA and LDA representations in multi-class classification and low-dimensional classification. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.imavis.2007.08.004 | Image Vision Comput. |
Keywords | Field | DocType |
pattern classification,multi-class classification,pattern representation,low-dimensional classification,dimensionality corresponds,lda,principle component analysis,representation frame structure,support vector machine,linear discriminate analysis,classification,lda representation,new representation,new distance-based separator representation,pca,multi class classification,feature space | Feature vector,Pattern recognition,Separator (oil production),Support vector machine,Curse of dimensionality,Artificial intelligence,Linear discriminant analysis,Linear classifier,Principal component analysis,Eigenvalues and eigenvectors,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 5 | Image and Vision Computing |
Citations | PageRank | References |
4 | 0.57 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Y. Shih | 1 | 1103 | 89.56 |
| Kai Zhang | 2 | 23 | 2.67 |