Results 31 to 40 of about 50,718 (252)
Biobjective sparse principal component analysis
Journal of Multivariate Analysis, 2014 Principal Components are usually hard to interpret. Sparseness is considered as one way to improve interpretability, and thus a trade-off between variance explained by the components and sparseness is frequently sought. In this note we address the problem of simultaneous maximization of variance explained and sparseness, and a heuristic method is ...
Emilio Carrizosa, Vanesa Guerrero
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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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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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Advances in Mechanical Engineering, 2017 In this article, for the first time, the optical flow and principal component analysis followed independent component analysis are combined for monitoring the motion process of robotic-arm-based system.
Song Fan +3 more
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Sparse Kernel Principal Component Analysis via Sequential Approach for Nonlinear Process Monitoring
IEEE Access, 2019 Kernel principal component analysis (KPCA) has been widely used for nonlinear process monitoring. However, since the principal components are linear combinations of all kernel functions, traditional KPCA suffers from poor interpretation and high ...
Lingling Guo +3 more
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Approximation Algorithms for Sparse Principal Component Analysis
CoRR, 2020 Principal component analysis (PCA) is a widely used dimension reduction technique in machine learning and multivariate statistics. To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis (SPCA).
Agniva Chowdhury +3 more
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IEEE Access, 2018 In this paper, we propose a data-driven model for predicting the travel speed of urban roads, based on GPS trajectories of vehicles. Though this is a strategically important task in many traffic monitoring systems, the problem has not yet been well ...
Liping Huang +4 more
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Journal of Big Data, 2021 Background New dipeptidyl peptidase-4 (DPP-4) inhibitors need to be developed to be used as agents with low adverse effects for the treatment of type 2 diabetes mellitus. This study aims to build quantitative structure-activity relationship (QSAR) models
Alhadi Bustamam +6 more
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eLife, 2023 Multivariable Mendelian randomisation (MVMR) is an instrumental variable technique that generalises the MR framework for multiple exposures. Framed as a regression problem, it is subject to the pitfall of multicollinearity.
Vasileios Karageorgiou +3 more
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Online Tensor Robust Principal Component Analysis
IEEE Access, 2022 Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In
Mohammad M. Salut, David V. Anderson
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