IEEE Trans Image Process - Discriminant learning through multiple principal angles for visual recognition.

Tópicos

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Resumo

Canonical correlation has been prevalent for multiset-based pairwise subspace analysis. As an extension, discriminant canonical correlations (DCCs) have been developed for classification purpose by learning a global subspace based on Fisher discriminant modeling of pairwise subspaces. However, the discriminative power of DCCs is not optimal as it only measures the "local" canonical correlations within subspace pairs, which lacks the "global" measurement among all the subspaces. In this paper, we propose a multiset discriminant canonical correlation method, i.e., multiple principal angle (MPA). It jointly considers both "local" and "global" canonical correlations by iteratively learning multiple subspaces (one for each set) as well as a global discriminative subspace, on which the angle among multiple subspaces of the same class is minimized while that of different classes is maximized. The proposed computational solution is guaranteed to be convergent with much faster converging speed than DCC. Extensive experiments on pattern recognition applications demonstrate the superior performance of MPA compared to existing subspace learning methods.

Resumo Limpo

canon correl preval multisetbas pairwis subspac analysi extens discrimin canon correl dccs develop classif purpos learn global subspac base fisher discrimin model pairwis subspac howev discrimin power dccs optim measur local canon correl within subspac pair lack global measur among subspac paper propos multiset discrimin canon correl method ie multipl princip angl mpa joint consid local global canon correl iter learn multipl subspac one set well global discrimin subspac angl among multipl subspac class minim differ class maxim propos comput solut guarante converg much faster converg speed dcc extens experi pattern recognit applic demonstr superior perform mpa compar exist subspac learn method

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