Journal ArticleDOI
Sparsity preserving projections with applications to face recognition
TLDR
A new unsupervised DR method called sparsity preserving projections (SPP), which aims to preserve the sparse reconstructive relationship of the data, which is achieved by minimizing a L1 regularization-related objective function.About:
This article is published in Pattern Recognition.The article was published on 2010-01-01. It has received 765 citations till now. The article focuses on the topics: Sparse approximation & Dimensionality reduction.read more
Citations
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Journal ArticleDOI
Maximum neighborhood margin discriminant projection for classification
TL;DR: A novel maximum neighborhood margin discriminant projection technique for dimensionality reduction of high-dimensional data that cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes.
Journal ArticleDOI
A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications
TL;DR: To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper.
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Robust Sparse Linear Discriminant Analysis
TL;DR: A novel feature extraction method called robust sparse linear discriminant analysis (RSLDA) is proposed to solve the above problems and achieves the competitive performance compared with other state-of-the-art feature extraction methods.
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Discriminant sparse neighborhood preserving embedding for face recognition
TL;DR: DSNPE not only preserves the sparse reconstructive relationship of SNPE, but also sufficiently utilizes the global discriminant structures from the following two aspects: maximum margin criterion (MMC) is added into the objective function of DSNPE.
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Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
TL;DR: A novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space is presented and a method to construct a sparse similarity graph, called L2-graph is introduced.
References
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Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Regularization and variable selection via the elastic net
Hui Zou,Trevor Hastie +1 more
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Journal ArticleDOI
Nonlinear dimensionality reduction by locally linear embedding.
Sam T. Roweis,Lawrence K. Saul +1 more
TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.