Results 41 to 50 of about 232,203 (264)
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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Large Covariance Estimation by Thresholding Principal Orthogonal Complements [PDF]
, 2013 This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-
Fan, Jianqing +2 more
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Sparse multivariate functional principal component analysis
Stat, 2022 We introduce a sparse multivariate functional principal component analysis method by incorporating ideas from the group sparse maximum variance method to multivariate functional data. Our method can avoid the “curse of dimensionality” from a high‐dimensional dataset and enjoy interpretability at the same time. In particular, our unsupervised method can
Jun Song, Kyongwon Kim
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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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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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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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Sparse Principal Component Analysis via Fractional Function Regularity [PDF]
Mathematical Problems in Engineering, 2020 In this paper, we describe a novel approach to sparse principal component analysis (SPCA) via a nonconvex sparsity-inducing fraction penalty function SPCA (FP-SPCA). Firstly, SPCA is reformulated as a fraction penalty regression problem model. Secondly, an algorithm corresponding to the model is proposed and the convergence of the algorithm is ...
Xuanli Han +3 more
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Weighted Increment Target Tracking Algorithm Based on Low-rank Sparse Representation [PDF]
Jisuanji gongcheng, 2016 The objects under complex environment often are affected by occlusion,illumination changes and so on,which lead to tracking drift.In order to improve the accuracy of visual tracking,a novel robust visual tracking algorithm is introduced by using Low-rank
YING Yanli,ZHANG Jiashu,QU Yao
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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 ...
Carrizosa Priego, Emilio José +1 more
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