Results 11 to 20 of about 13,553,800 (229)
Structural components in functional data. [PDF]
, 2009 Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space.
Gasser, Theo +2 more
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Sparse principal component analysis for natural language processing [PDF]
, 2023 High dimensional data are rapidly growing in many different disciplines, particularly in natural language processing. The analysis of natural language processing requires working with high dimensional matrices of word embeddings obtained from text data ...
Drikvandi, Reza, Lawal, Olamide
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Bayesian Orthogonal Component Analysis for Sparse Representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
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Exactly Uncorrelated Sparse Principal Component Analysis
, 2023 Sparse principal component analysis (PCA) aims to find principal components as linear combinations of a subset of the original input variables without sacrificing the fidelity of the classical PCA.
Oh-Ran Kwon (16510770) +2 more
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Principal component and factor analysis to study variations in the aging lumbar spine [PDF]
, 2015 Human spine is a multifunctional structure of human body consisting of bones, joints, ligaments and muscles which all undergo a process of change with the age. A sudden change in these features either naturally or thorough injury can lead to some serious
Shah, Akeel A. +4 more
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Iterative Temporal Learning and Prediction with the Sparse Online Echo State Gaussian Process [PDF]
, 2012 16/01/14 meb. pre-print version OK to add. statement added.In this work, we contribute the online echo state gaussian process (OESGP), a novel Bayesian-based online method that is capable of iteratively learning complex temporal dynamics and producing ...
Soh, Harold +3 more
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Eigenvectors from Eigenvalues Sparse Principal Component Analysis
, 2021 We present a novel technique for sparse principal component analysis. This method, named eigenvectors from eigenvalues sparse principal component analysis (EESPCA), is based on the formula for computing squared eigenvector loadings of a Hermitian matrix ...
H. Robert Frost (10994582)
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Sparse representations in audio & music: from coding to source separation [PDF]
, 2010 —Sparse representations have proved a powerful toolin the analysis and processing of audio signals and already lieat the heart of popular coding standards such as MP3 andDolby AAC.
Davies, ME +15 more
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Fast Dictionary Learning for Sparse Representations of Speech Signals [PDF]
, 2011 © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new ...
Maria G. Jafari +3 more
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“SFM”: An R package for Skew Factor Models
SoftwareXThe goal of the Skew Factor Model (SFM) package is to analyze datasets where factor loading matrices exhibit skewed distributions, providing efficient estimation methods for SFM. The proposed R package, called SFM, is specifically designed for estimating
Yu Jin, Guangbao Guo
doaj +1 more source