Abstract In this paper, we generalize the Extended Redundancy Analysis (ERA), extending this new class of models to allow for external covariate effects. In particular, covariates are allowed to affect endogenous indicators indirectly through the composites and/or directly. The method proposed

derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a

analysis, based on the non-deterministic complexity of an instance. In this analysis we prove that there is a deterministic algorithm asymptotically performing as well as any randomized algorithm in the comparison model. We define the redundancy analysis, based on a measure of the internal redundancy

Abstract Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures redundant information in the criterion variables in a most parsimonious way. A ridge type of regularization

Abstract Lazraq and Cléroux (Psychometrika, 2002, 411-419) proposed a test for identifying the number of significant components in redundancy analysis. This test, however, is ill conceived. A major problem is that it regards each redundancy component as if it were a single observed predictor

in order to improve accuracy and stability. This research presents an analysis of the effect of removing irrelevant and redundant features with ensemble classifiers using five datasets and compared with floating search method. Eliminating redundant features provides better accuracy and computational time

Miller [12] for a general discussion and bibliography). Events ordered in time are particularly amenable to informational analysis; thus language sequences are being extensively studied, and other sequences, such as those of music, plainly invite research. In this paper I shall indicate some of the ways

This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho’s basis pursuit (BP) paradigm can recover the optimal

. GAF moderates this policy using application- and system-level information; nodes that source or sink data remain on and intermediate nodes monitor and balance energy use. GAF is independent of the underlying ad hoc routing protocol; we simulate GAF over unmodified AODV and DSR. Analysis and simulation

A three-layered neural network is described for transforming two-dimensional discrete signals into generalized nonorthogonal 2-D “Gabor” representations for image analysis, segmentation, and compression. These transforms are conjoint spatial/spectral representations [lo], [15], which provide a