Journal ArticleDOI
Face recognition via Weighted Sparse Representation
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TLDR
A locality Weighted Sparse Representation based Classification (WSRC) method is proposed, which utilizes both data locality and linearity; it can be regarded as extensions of SRC, but the coding is local.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2013-02-01. It has received 229 citations till now. The article focuses on the topics: k-nearest neighbors algorithm & Sparse approximation.read more
Citations
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Journal ArticleDOI
Two-stage plant species recognition by local mean clustering and Weighted sparse representation classification
TL;DR: Experimental results on the leaf image database demonstrate that the proposed two-stage local similarity based classification learning method not only has a high accuracy and low time cost, but also can be clearly interpreted.
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Discriminative Block-Diagonal Representation Learning for Image Recognition
TL;DR: The proposed discriminative block-diagonal low-rank representation (BDLRR) method for recognition not only shows superior potential on image recognition but also outperforms the state-of-the-art methods.
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Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding
TL;DR: A novel computational model combining weighted sparse representation based classifier (WSRC) and global encoding (GE) of amino acid sequence is introduced to predict protein interaction class and is a very efficient method to predict PPIs and may be a useful supplementary tool for future proteomics studies.
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Generalized Uncorrelated Regression with Adaptive Graph for Unsupervised Feature Selection
TL;DR: An improved sparse regression model [generalized uncorrelated regression model (GURM)] for seeking the uncor related yet discriminative features is presented and a graph regularization term based on the principle of maximum entropy is incorporated into the GURM model (URAFS), so as to embed the local geometric structure of data into the manifold learning.
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A survey on representation-based classification and detection in hyperspectral remote sensing imagery
TL;DR: This paper reviews the state-of-the-art representation-based classification and detection approaches for hyperspectral remote sensing imagery, including sparse representation-Based classification (SRC), collaborative representation- based classification (CRC), and their extensions.
References
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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.
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Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.