Results 41 to 50 of about 50,718 (252)
IEEE Access, 2017 Since microaneurysms (MAs) can be seen as the earliest lesions in diabetic retinopathy, its detection plays a critical role in the diabetic retinopathy diagnosis.
Wei Zhou +4 more
doaj +1 more source
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
doaj +1 more source
Large-scale paralleled sparse principal component analysis [PDF]
Multimedia Tools and Applications, 2014 Principal component analysis (PCA) is a statistical technique commonly used in multivariate data analysis. However, PCA can be difficult to interpret and explain since the principal components (PCs) are linear combinations of the original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and interpretability by approximating sparse PCs ...
Weifeng Liu 0001 +4 more
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Sparse principal component analysis in medical shape modeling [PDF]
SPIE Proceedings, 2006 Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings ...
Sjöstrand, Karl +2 more
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Establishing a comprehensive apheresis medicine program in a resource‐constrained setting presents significant structural, financial, and logistical challenges. Despite the growing clinical importance of apheresis services globally, published experience from sub‐Saharan Africa remains sparse.
Folasade Adelekan‐Popoola +4 more
wiley +1 more source
Optimal Solutions for Sparse Principal Component Analysis
J. Mach. Learn. Res., 2007 Revised journal version. More efficient optimality conditions and new examples in subset selection and sparse recovery.
Alexandre d'Aspremont +2 more
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin +2 more
wiley +1 more source
Generalized power method for sparse principal component analysis [PDF]
J. Mach. Learn. Res., 2008 In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively.
Journée, Michel +3 more
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Diversity and complexity in neural organoids
FEBS Letters, EarlyView.Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
An augmented Lagrangian approach for sparse principal component analysis [PDF]
Mathematical Programming, 2011 Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduction with numerous applications in science and engineering. However, the standard PCA suffers from the fact that the principal components (PCs) are usually linear combinations of all the original variables, and it is thus often difficult to interpret the ...
Zhaosong Lu, Yong Zhang 0004
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