Name
Title | ||
|---|---|---|
A Theoretical Framework For Matrix-Based Feature Extraction Algorithms With Its Application To Image Recognition |
Abstract | ||
|---|---|---|
Recently proposed matrix-based methods, two-dimensional Principal Component Analysis (2DPCA), two-dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Locality Preserving Projections (2DLPP) have been shown to be effective ways to avoid the problems of high dimensionality and small sample sizes that are associated with vector-based methods. In this paper, we propose a general theoretical framework for matrix-based feature extraction algorithms from the point of view of graph embedding. Our framework can be applied to extend two recently proposed vector-based algorithms, i.e. Unsupervised Discriminant Projection (UDP) and Marginal Fisher Analysis (MFA) algorithms, to their matrix-based versions. Further, our framework can also be used as a platform to generate new matrix-based feature extraction algorithms by designing meaningful graphs, e.g. two-dimensional Discriminant Embedding Analysis (2DDEA) in this paper. It is shown that 2DLDA is actually a special case of the 2DDEA method. Experiments on three publicly available image databases demonstrate the effectiveness of the proposed algorithm. Our results fit into the scene for a better picture about the matrix-based feature extraction algorithms. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1142/S0219467808002940 | INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS |
Keywords | Field | DocType |
Matrix-based methods, theoretical framework, two-dimensional PCA (2DPCA), two-dimensional LDA (2DLDA), two-dimensional LPP (2DLPP), two-dimensional discriminant embedding analysis (2DDEA) | Computer vision,Dimensionality reduction,Embedding,Pattern recognition,Discriminant,Matrix (mathematics),Graph embedding,Curse of dimensionality,Artificial intelligence,Linear discriminant analysis,Mathematics,Principal component analysis | Journal |
Volume | Issue | ISSN |
8 | 1 | 0219-4678 |
Citations | PageRank | References |
3 | 0.44 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guiyu Feng | 1 | 174 | 9.92 |
| David Zhang | 2 | 7365 | 360.85 |
| Jian Yang | 3 | 8 | 1.55 |
| Dewen Hu | 4 | 1290 | 101.20 |