Sibao Chen

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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)
Many well-known machine learning and pattern recognition methods can be seen as special cases of sparse minimization of Positive Definite Quadratic Forms (PDQF). An algorithm framework of sparse minimization is proposed for PDQF. It is theoretically analyzed to converge to global minimum. The computational complexity is analyzed and compared with the(More)