Name
Abstract | ||
|---|---|---|
Dimensionality reduction via manifold learning offers an elegant representation of data whereby the high dimensional feature space is parameterized by a lower dimensional space where the data resides. Sparse representations efficiently represent test patterns by sparse linear coefficients from a dictionary of training exemplars. Sparse representations have been adopted for classification purposes, but the resulting classifiers may have to deal with data in high dimensions and large dictionaries. This paper analyzes the interaction between dimensionality reduction and sparse representations. The proposed technique, called K-LGE, presents a unified framework which utilizes a semi-supervised variant of Linear extension of Graph Embedding with K-SVD dictionary learning. An iterative procedure optimizes the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and linear classifier. Results are demonstrated in a wide variety of facial and activity recognition problems to demonstrate the robustness of our proposed method. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FG.2013.6553786 | FG |
Keywords | Field | DocType |
data structures,dictionaries,face recognition,graph theory,image classification,iterative methods,learning (artificial intelligence),matrix algebra,optimisation,K-LGE,K-SVD dictionary learning,activity recognition,classification purposes,data representation,dimensionality reduction matrix,facial recognition,graph embedding,high dimensional feature space,iterative procedure,joint optimization,linear classifier,linear extension,lower dimensional space,manifold learning,semisupervised variant,sparse coefficients,sparse linear coefficients,sparse representation dictionary,sparse representations,test patterns,training exemplars,activity recognition,dimensionality reduction,facial analysis,manifold learning,sparse representation | Sparse PCA,Feature vector,Dimensionality reduction,Pattern recognition,K-SVD,Sparse approximation,Artificial intelligence,Linear classifier,Nonlinear dimensionality reduction,Mathematics,Sparse matrix | Conference |
ISSN | Citations | PageRank |
2326-5396 | 2 | 0.37 |
References | Authors | |
17 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raymond W. Ptucha | 1 | 113 | 22.42 |
| Andreas Savakis | 2 | 377 | 41.10 |