In this paper, a combined nonlinear dimensionality reduction and multiclass classification framework is proposed. Specifically, a novel discriminant analysis (DA) technique, called accelerated kernel subclass discriminant analysis (AKSDA), derives a discriminant subspace, and a linear multiclass support vector machine (MSVM) computes a set of separating hyperplanes in the derived subspace. Moreover, within this framework an approach for accelerating the computation of multiple Gram matrices and an associated late fusion scheme are presented. Experimental evaluation in five multimedia datasets, on tasks such as video event detection and news document classification, shows that the proposed framework achieves excellent results in terms of both training time and generalization performance.

In this paper a discriminant analysis (DA) technique called accelerated generalised subclass discriminant analysis (AGSDA) and its GPU implementation are presented. This method identifies a discriminant subspace of the input space in three steps: a) Gram matrix computation, b) eigenvalue decomposition of the between subclass factor matrix, and c) computation of the solution of a linear matrix system with symmetric positive semidefinite (SPSD) matrix of coefficients. Based on the fact that the computationally intensive parts of AGSDA, i.e. Gram matrix computation and identification of the SPSD linear matrix system solution, are highly parallelisable, a GPU implementation of AGSDA is proposed. Experimental results on large-scale datasets of TRECVID for event and concept detection show that our GPU-AGSDA method combined with LSVM outperforms LSVM alone in training time, memory consumption, and detection accuracy.