Incremental Accelerated Kernel Discriminant Analysis
Nikolaos Gkalelis, Vasileios Mezaris · 2017
In this paper a novel incremental dimensionality reduction (DR) technique called incremental accelerated kernel discriminant analysis (IAKDA) is proposed. Consisting of the eigenvalue decomposition of a relatively small-size matrix and the recursive block Cholesky factorization of the kernel matrix, a nonlinear DR transformation is efficiently computed at each incremental step. Moreover, employing factorization techniques of excellent numerical stability, IAKDA effectively removes data nonlinearities in the low dimensional subspace. Experimental evaluation on various multimedia tasks and datasets confirms that the proposed approach combined with linear support vector machines (LSVMs) offers improved mean average precision (MAP) and provides an impressive training time speedup over batch KDA and also over traditional LSVM and kernel SVM (KSVM).