Results 61 to 70 of about 90,827 (166)
Rational Polynomial Coefficient Estimation via Adaptive Sparse PCA-Based Method
Remote SensingThe Rational Function Model (RFM) is composed of numerous highly correlated Rational Polynomial Coefficients (RPCs), establishing a mathematical relationship between two-dimensional images and three-dimensional spatial coordinates.
Tianyu Yan, Yingqian Wang, Pu Wang
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Weighted Low Rank Approximation for Background Estimation Problems
, 2017 Classical principal component analysis (PCA) is not robust to the presence of sparse outliers in the data. The use of the $\ell_1$ norm in the Robust PCA (RPCA) method successfully eliminates the weakness of PCA in separating the sparse outliers. In this
Dutta, Aritra, Li, Xin
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Applied Sciences, 2023 Short-term wind forecasting is critical for the dispatch, controllability and stability of a power grid. As a challenging but indispensable work, short-term wind forecasting has attracted considerable attention from researchers.
Shi Liu, Guangchao Zhang
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Sparse PCA for High-Dimensional Data With Outliers [PDF]
Technometrics, 2016 A new sparse PCA algorithm is presented, which is robust against outliers. The approach is based on the ROBPCA algorithm that generates robust but nonsparse loadings. The construction of the new ROSPCA method is detailed, as well as a selection criterion for the sparsity parameter.
Hubert, Mia +3 more
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Sparse PCA via Covariance Thresholding
J. Mach. Learn. Res., 2013 40 pages, 3 figures ...
Yash Deshpande, Andrea Montanari
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Sparse HJ Biplot: A New Methodology via Elastic Net
Mathematics, 2021 The HJ biplot is a multivariate analysis technique that allows us to represent both individuals and variables in a space of reduced dimensions. To adapt this approach to massive datasets, it is necessary to implement new techniques that are capable of ...
Mitzi Cubilla-Montilla +3 more
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Fast Randomized PCA for Sparse Data
CoRR, 2018 Principal component analysis (PCA) is widely used for dimension reduction and embedding of real data in social network analysis, information retrieval, and natural language processing, etc. In this work we propose a fast randomized PCA algorithm for processing large sparse data.
Xu Feng +4 more
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IEEE Access, 2023 Diversity measures exploited by blind source separation (BSS) methods are usually based on either statistical attributes/geometrical structures or sparse/overcomplete (underdetermined) representations of the signals.
Muhammad Usman Khalid +2 more
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, 2017 We study the problem of detecting a structured, low-rank signal matrix corrupted with additive Gaussian noise. This includes clustering in a Gaussian mixture model, sparse PCA, and submatrix localization.
Banks, Jess +4 more
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Low-rank and eigenface based sparse representation for face recognition. [PDF]
PLoS ONE, 2014 In this paper, based on low-rank representation and eigenface extraction, we present an improvement to the well known Sparse Representation based Classification (SRC).
Yi-Fu Hou +3 more
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