Ioannis Pitas

TL;DR: The proposed Approximate Kernel Extreme Learning Machine algorithm for Single-hidden Layer Feedforward Neural network training that can be used for large scale classification problems is able to scale well in both computational cost and memory, while achieving good generalization performance.

TL;DR: The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information.

TL;DR: A novel sequence representation, based on its fuzzy distances from optimal representative signal instances, called statemes, is proposed, and a novel modified clustering discriminant analysis algorithm minimizing the adopted criterion leading to the optimal discriminant sequence class representation in a low-dimensional space, respectively.

TL;DR: The proposed approach is tested on three problems relating to human behavior analysis: Face recognition, facial expression recognition, and human action recognition, since the proposed class-specific reference discriminant analysis outperforms kernel discriminantAnalysis, kernel spectral regression, and class- specific kernel discriminatory analysis, as well as support vector machine-based classification, in most cases.

TL;DR: An algorithm to cluster face images found in video sequences is proposed, and a novel method for creating a dissimilarity matrix using SIFT image features is introduced, which yields the clustering result.