Results 51 to 60 of about 513,547 (285)
Class-Specific Sparse Principal Component Analysis for Visual Classification
IEEE Access, 2020 Extensive research has demonstrated that dictionary learning is active in improving the performance of the representation based classification. However, dictionary learning suffers from lacking an effective dictionary structure that can well tradeoff the
Fei Pan +3 more
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SUPER-RESOLUTION OF HYPERSPECTRAL IMAGES USING COMPRESSIVE SENSING BASED APPROACH [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Over the past decade hyper spectral (HS) image analysis has turned into one of the most powerful and growing technologies in the field of remote sensing.
R. C. Patel, M. V. Joshi
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Sparse Kernel PCA for Outlier Detection
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018 Accepted at IEEE ICMLA 2018 for Oral ...
Rudrajit Das +2 more
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A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes
, 2016 Pearson’s correlation measure is only able to model linear dependence between random variables. Hence, conventional principal component analysis (PCA) based on Pearson’s correlation measure is not suitable for application to modern industrial processes ...
Yu, Hongyang +5 more
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Advanced Functional Materials, EarlyView.Schematic illustration of LNP‐MPG nuclei‐targeting delivery of HMW‐FGF2 promoting histone acetylation to regulate the fate of DPSCs and treat spinal cord injury. LNPs components include pHMW‐FGF2 plasmid, DSPC, Dlin‐MC3‐DMA, cholesterol, and PEG2000, and are modified with MPG to form HMW‐FGF2@LNP‐MPG (HLM). HLM nuclei‐targets DPSCs to deliver HMW‐FGF2,
Heng Zhou +6 more
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Incremental Sparse Adaptive PCA for Streaming Industrial Sensor Data
TelecomIndustrial Internet of Things (IIoT) systems generate high-dimensional, non-stationary sensor streams under strict memory and computational constraints, limiting the applicability of classical batch dimensionality reduction methods. While incremental PCA
Rebin Saleh, Balázs Villányi
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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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Advanced Materials, EarlyView.In this study, we show that retinal matrix stiffness is increased in diabetic mice. Using engineered human vascular models with controlled matrix stiffness, we demonstrate that stiffening activates focal adhesion kinase (FAK) signaling in pericytes, suppresses Notch signaling, and disrupts vascular morphology.
Emily Warren +6 more
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Exact and Approximation Algorithms for Sparse PCA
CoRR, 2020 49 pages, 1 ...
Yongchun Li, Weijun Xie 0001
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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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