Name
Abstract | ||
|---|---|---|
Linear Discriminant Analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of this method is that it may encounter the small sample size problem in practice. In this paper, we present a novel LDA approach for high-dimensional data. Instead of direct dimension reduction using PCA as the first step, the high-dimensional data are mapped into a relatively lower dimensional similarity space, and then the LDA technique is applied. The preliminary experimental results on the ORL face database verify the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11539087_23 | ICNC (1) |
Keywords | Field | DocType |
major drawback,linear discriminant analysis,direct dimension reduction,high-dimensional data,feature extraction,orl face database,novel lda approach,lda technique,dimensional similarity space,dimension reduction,high dimensional data | Similitude,Clustering high-dimensional data,Dimensionality reduction,Pattern recognition,Computer science,Projection (linear algebra),Feature extraction,Artificial intelligence,Linear discriminant analysis,Online analytical processing,Principal component analysis | Conference |
Volume | ISSN | ISBN |
3610 | 0302-9743 | 3-540-28323-4 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guiyu Feng | 1 | 174 | 9.92 |
| Dewen Hu | 2 | 1290 | 101.20 |
| Ming Li | 3 | 13 | 4.67 |
| Zongtan Zhou | 4 | 412 | 33.89 |