Results 31 to 40 of about 513,547 (285)
Sparse PCA from Sparse Linear Regression
CoRR, 2018 To appear in NeurIPS ...
Bresler, Guy +2 more
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IEEE Access, 2018 In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning.
Fei Wen +3 more
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NP-hardness and inapproximability of sparse PCA [PDF]
Information Processing Letters, 2017 We give a reduction from {\sc clique} to establish that sparse PCA is NP-hard. The reduction has a gap which we use to exclude an FPTAS for sparse PCA (unless P=NP). Under weaker complexity assumptions, we also exclude polynomial constant-factor approximation algorithms.
openaire +3 more sources
Using ℓ1-Relaxation and Integer Programming to Obtain Dual Bounds for Sparse PCA
, 2022 Dual Bounds of Sparse Principal Component Analysis Sparse principal component analysis (PCA) is a widely used dimensionality reduction tool in machine learning and statistics. Compared with PCA, sparse PCA enhances the interpretability by incorporating
Dey, Santanu S +5 more
core +1 more source
A greedy anytime algorithm for sparse PCA
CoRR, 2019 The taxing computational effort that is involved in solving some high-dimensional statistical problems, in particular problems involving non-convex optimization, has popularized the development and analysis of algorithms that run efficiently (polynomial-time) but with no general guarantee on statistical consistency.
Guy Holtzman, Adam Soffer, Dan Vilenchik
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Face Recognition Based on Sparse Two-Direction Two-Dimensional Principle Component Analysis [PDF]
Jisuanji gongcheng, 2019 Two-Direction Two-Dimensional Principle Component Analysis((2D)2PCA) is an improved method of Principle Component Analysis(PCA) in the two-dimensional space.However,just like PCA,the (2D)2PCA is susceptible to abnormal values,its robustness is weak and ...
ZHANG Yuping, GONG Xiaofeng, LUO Ruisen
doaj +1 more source
Optimal Sparse Linear Encoders and Sparse PCA [PDF]
, 2020 Principal components analysis (PCA) is the optimal linear encoder of data. Sparse linear encoders (e.g., sparse PCA) produce more interpretable features that can promote better generalization. (i) Given a level of sparsity, what is the best approximation
Christos Boutsidis +3 more
core
Sparse PCA: Algorithms, Adversarial Perturbations and Certificates [PDF]
2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS), 2020 We study efficient algorithms for Sparse PCA in standard statistical models (spiked covariance in its Wishart form). Our goal is to achieve optimal recovery guarantees while being resilient to small perturbations. Despite a long history of prior works, including explicit studies of perturbation resilience, the best known algorithmic guarantees for ...
Tommaso d'Orsi +3 more
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IEEE Access, 2017 Since microaneurysms (MAs) can be seen as the earliest lesions in diabetic retinopathy, its detection plays a critical role in the diabetic retinopathy diagnosis.
Wei Zhou +4 more
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Craniofacial similarity analysis through sparse principal component analysis.
PLoS ONE, 2017 The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery.
Junli Zhao +7 more
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