TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Class-specific nonlinear subspace learning based on optimized class representation

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2015 23rd European Signal Processing Conference (EUSIPCO)
Sivut2491 - 2495
DOI - pysyväislinkit
TilaJulkaistu - 2015
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa

Tiivistelmä

In this paper, a new nonlinear subspace learning technique for class-specific data representation based on an optimized class representation is described. An iterative optimization scheme is formulated where both the optimal nonlinear data projection and the optimal class representation are determined at each optimization step. This approach is tested on human face and action recognition problems, where its performance is compared with that of the standard class-specific subspace learning approach, as well as other nonlinear discriminant subspace learning techniques. Experimental results denote the effectiveness of this new approach, since it consistently outperforms the standard one and outperforms other nonlinear discriminant subspace learning techniques in most cases.