Results 41 to 50 of about 564,038 (269)
Structured Sparse Principal Component Analysis
Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2009 We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This \emph{structured sparse PCA} is based on a structured regularization recently introduced by [1].
R. Jenatton, G. Obozinski, F. Bach
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Robust sparse principal component analysis: situation of full sparseness
Journal of Applied Mathematics, Statistics and Informatics, 2022 Abstract Principal Component Analysis (PCA) is the main method of dimension reduction and data processing when the dataset is of high dimension. Therefore, PCA is a widely used method in almost all scientific fields. Because PCA is a linear combination of the original variables, the interpretation process of the analysis results is often
Alkan, Bilal Barış, Ünaldi, Işıl
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A flexible framework for sparse simultaneous component based data integration
BMC Bioinformatics, 2011 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect.
Van Deun Katrijn +4 more
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Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis
Measurement Science Review, 2018 This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion.
Andráš Imrich +3 more
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Fast dictionary learning from incomplete data
EURASIP Journal on Advances in Signal Processing, 2018 This paper extends the recently proposed and theoretically justified iterative thresholding and K residual means (ITKrM) algorithm to learning dictionaries from incomplete/masked training data (ITKrMM).
Valeriya Naumova, Karin Schnass
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Scaling Law for Recovering the Sparsest Element in a Subspace [PDF]
, 2014 We address the problem of recovering a sparse $n$-vector within a given subspace. This problem is a subtask of some approaches to dictionary learning and sparse principal component analysis.
Demanet, Laurent, Hand, Paul
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Sparse Principal Components Analysis
, 2009 This manuscript was written in late 2003; a much revised version is to appear, with discussion and later references, in the Journal of the American Statistical Association in 2009.
Lu, Arthur, Johnstone, Iain
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Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis
Energies, 2022 In the acoustics-based power transformer fault diagnosis, a transformer acoustic signal collected by an acoustic sensor is generally mixed with a large number of interference signals.
Guo Wang +3 more
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Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis [PDF]
Journal of Mass Spectrometry, 2009 AbstractThe paper presents sparse component analysis (SCA)‐based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures
Kopriva, Ivica, Jerić, Ivanka
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High Dimensional Semiparametric Scale-Invariant Principal Component Analysis
, 2014 We propose a new high dimensional semiparametric principal component analysis (PCA) method, named Copula Component Analysis (COCA). The semiparametric model assumes that, after unspecified marginally monotone transformations, the distributions are ...
Han, Fang, Liu, Han
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