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Allen Y. Yang
Researcher at University of California, Berkeley
Publications - 120
Citations - 15154
Allen Y. Yang is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Sparse approximation & Facial recognition system. The author has an hindex of 37, co-authored 117 publications receiving 14216 citations. Previous affiliations of Allen Y. Yang include Montclair State University & Honda.
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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.
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Unsupervised segmentation of natural images via lossy data compression
TL;DR: This paper model the distribution of the texture features using a mixture of Gaussian distributions, allowing the mixture components to be degenerate or nearly-degenerate, and shows that such a mixture distribution can be effectively segmented by a simple agglomerative clustering algorithm derived from a lossy data compression approach.
Proceedings ArticleDOI
Fast ℓ 1 -minimization algorithms and an application in robust face recognition: A review
TL;DR: A comprehensive review of five representative ℓ1-minimization methods, i.e., gradient projection, homotopy, iterative shrinkage-thresholding, proximal gradient, and augmented Lagrange multiplier, for face recognition is provided.
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Fast $\ell_{1}$ -Minimization Algorithms for Robust Face Recognition
TL;DR: In this paper, the authors focus on the numerical implementation of a sparsity-based classification framework in robust face recognition, where sparse representation is sought to recover human identities from high-dimensional facial images that may be corrupted by illumination, facial disguise, and pose variation.
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Estimation of Subspace Arrangements with Applications in Modeling and Segmenting Mixed Data
TL;DR: This paper provides a comprehensive summary of important algebraic properties and statistical facts that are crucial for making the inference of subspace arrangements both efficient and robust, even when the given data are corrupted by noise or contaminated with outliers.