Results 21 to 30 of about 564,038 (269)
Frontiers in Energy Research, 2023 The extensive application of power electronic equipment and the increasing penetration of renewable energy generation gradually strengthen the nonlinear and modal-coupling characteristics of electromechanical oscillation of modern power systems.
Zhiwei Wang +4 more
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Robust Sparse Component Analysis Based on a Generalized Hough Transform
EURASIP Journal on Advances in Signal Processing, 2007 An algorithm called Hough SCA is presented for recovering the matrix A in x(t)=As(t), where x(t) is a multivariate observed signal, possibly is of lower dimension than the unknown sources s(t).
Andrzej Cichocki +2 more
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Sparse bounded component analysis
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016 Bounded Component Analysis (BCA) is a recent approach which enables the separation of both dependent and independent signals from their mixtures. This article introduces a novel deterministic instantaneous BCA approach for the separation of sparse bounded sources.
Babatas, Eren, Erdogan, Alper T.
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Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis. [PDF]
PLoS ONE, 2015 Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the
Nan Lin +3 more
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IEEE Access, 2021 The underdetermined blind source separation (UBSS) has been considered to be a novel signal processing technique, which can separate the fault source signals from their mixtures.
Jindong Wang +4 more
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Weighted sparse principal component analysis
Chemometrics and Intelligent Laboratory Systems, 2019 Sparse principal component analysis (SPCA) has been shown to be a fruitful method for the analysis of high-dimensional data. So far, however, no method has been proposed that allows to assign elementwise weights to the matrix of residuals, although this may have several useful applications.
Van Deun, Katrijn +6 more
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Sparse principal component based high-dimensional mediation analysis [PDF]
Computational Statistics & Data Analysis, 2020 Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. With multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators.
Yi Zhao +2 more
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Sparse Exploratory Factor Analysis [PDF]
, 2017 Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables.
A Edelman +21 more
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Sparse Principal Component Analysis for Natural Language Processing [PDF]
Annals of Data Science, 2020 AbstractHigh 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. Those matrices are often sparse in the sense that they contain many zero elements.
Drikvandi, Reza, Lawal, Olamide
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Meta Sparse Principal Component Analysis
, 2022 29 pages, 7 ...
Banerjee, Imon, Honorio, Jean
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