With very noisy data, having plentiful samples eliminates overfitting in nonlinear regression, but not in nonlinear principal component analysis (NLPCA). To overcome this problem...
In this paper we present a fusion technique for Support Vector Machine (SVM) scores, obtained after a dimension reduction with Bilateralprojection-based Two-Dimensional Principal C...
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
In this paper, we introduce a new approach for content-based similarity search for brain images. Based on the keyblock representation, our framework employs the Principal Componen...
In this paper, the problem of discovering anomalies in a large-scale network based on the data fusion of heterogeneous monitors is considered. We present a classification of anoma...
A method based on Principal Component Analysis of the fingernail coloration pattern is presented to infer fingertip force direction during planar contact. Images from 7 subjects...
This paper presents the design and real-time implementation of a fall-detection system, aiming at detecting fall incidents in unobserved home situations. The setup employs two fix...
Lykele Hazelhoff, Jungong Han, Peter H. N. de With
—Beyond conventional linear and kernel-based feature extraction, we present a more generalized formulation for feature extraction in this paper. Two representative algorithms usi...
Wikipedia is the largest monolithic repository of human knowledge. In addition to its sheer size, it represents a new encyclopedic paradigm by interconnecting articles through hyp...
—In this paper, we describe and compare three Collaborative Filtering (CF) algorithms aiming at the low-rank approximation of the user-item ratings matrix. The algorithm implemen...
Manolis G. Vozalis, Angelos I. Markos, Konstantino...