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Dimensionality reduction is usually involved in the domains of artificial intelligence and machine learning. Linear projection of features is of particular interest for dimensionality reduction since it is simple to calculate and analytically analyze. In this paper, we propose an essentially linear projection technique, called locality-preserved maximum(More)
Lasso-type variable selection has increasingly expanded its machine learning applications. In this paper, un-correlated Lasso is proposed for variable selection, where variable de-correlation is considered simultaneously with variable selection, so that selected variables are uncorrelated as much as possible. An effective iterative algorithm, with the proof(More)
Without constructing adjacency graph for neighborhood, we propose a method to learn similarity among sample points of manifold in Laplacian embedding (LE) based on adding constraints of linear reconstruction and least absolute shrinkage and selection operator type minimization. Two algorithms and corresponding analyses are presented to learn similarity for(More)
Pedestrian identification is a very important topic in the area of intelligent surveillance and public safety, where the near front face images of pedestrian can hardly be obtained due to high installation angle of camera, long-distance location and extreme light variations. This paper presents a new action-based pedestrian identification algorithm, which(More)