Results 91 to 100 of about 232,203 (264)

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, Zhan-Li Sun, Yan-Wen Chong, Chun-Hou Zheng   +3 more
doaj   +1 more source

Sparse Principal Component Analysis with Missing Observations [PDF]

, 2013
In this paper, we study the problem of sparse Principal Component Analysis (PCA) in the high-dimensional setting with missing observations. Our goal is to estimate the first principal component when we only have access to partial observations. Existing estimation techniques are usually derived for fully observed data sets and require a prior knowledge ...
openaire   +2 more sources

Structure–Function Decoupling of the Sensorimotor and Default Mode Networks in Black Americans With MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano, Giacomo Boffa, Maria Petracca, Marta Ponzano, Nicole Graziano, Claire Wigley, Claire Riley, Jonathan Howard, Pietro Bontempi, Sylvia Klineova, Fred Lublin, Matilde Inglese   +11 more
wiley   +1 more source

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
core   +1 more source

Effectiveness of rTMS on Working Memory and Inhibitory Impairments in Patients With Post‐Stroke Executive Deficits

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Considerable efforts have been dedicated to developing effective treatments for post‐stroke executive impairment (PSEI), among which repetitive transcranial magnetic stimulation (rTMS) has shown great potential. This study aimed to investigate the therapeutic effects of high‐frequency rTMS on working memory (WM) and response ...
Mengting Lao, Rongwei Du, Zhujun Wang, Yang Gao, Meiling Cheng, Xianglong Wang, Wen Wu   +6 more
wiley   +1 more source

Risk factor analysis for stunting incidence using sparse categorical principal component logistic regression

MethodsX
The risk factors for stunting incidence involve categorical data in both the response and predictor variables. Therefore, we developed a sparse categorical principal component logistic regression model capable of handling data with multicollinearity. The
Anna Islamiyati, Muhammad Nur, Abdul Salam, Wan Zuki Azman Wan Muhamad, Dwi Auliyah   +4 more
doaj   +1 more source

Iteratively Reweighted Least Squares Algorithm for Sparse Principal Component Analysis with Application to Voting Records [PDF]

Statistika: Statistics and Economy Journal, 2017
Principal component analysis (PCA) is a popular dimensionality reduction and data visualization method. Sparse PCA (SPCA) is its extensively studied and NP-hard-to-solve modifcation. In the past decade, many diferent algorithms were proposed to perform
Tomáš Masák
doaj  

Evaluation of a Novel Electric Health Record Sidecar Application to Display Rheumatoid Arthritis Clinical Outcomes During Clinic Visits: Results of a Stepped‐Wedge Cluster Randomized Pragmatic Trial

Arthritis Care &Research, EarlyView.
Objective We developed a novel electronic health record sidecar application to visualize key rheumatoid arthritis (RA) outcomes, including disease activity, physical function, and pain, via a patient‐facing graphical interface designed for use during outpatient visits (“RA PRO dashboard”).
Gabriela Schmajuk, Jing Li, Cathy Nasrallah, Cherish Wilson, Alicia Hamblin, Christine Hariz, Cammie Young, Stephen Shiboski, Emma Kersey, Mary Nakamura, Judith F. Ashouri, Mehrdad Matloubian, Andrew J. Gross, Jennifer Barton, Beth Berrean, Jonathan Prugh, Jinoos Yazdany   +16 more
wiley   +1 more source

An improved nonlocal sparse regularization-based image deblurring via novel similarity criteria

International Journal of Advanced Robotic Systems, 2018
Image deblurring is a challenging problem in image processing, which aims to reconstruct an original high-quality image from its blurred measurement caused by various factors, for example, imperfect focusing caused by the imaging system or different ...
Nannan Wang, Wenxuan Shi, Ci’en Fan, Lian Zou   +3 more
doaj   +1 more source

Sparse Principal Components Analysis: a Tutorial

, 2021
The topic of this tutorial is Least Squares Sparse Principal Components Analysis (LS SPCA) which is a simple method for computing approximated Principal Components which are combinations of only a few of the observed variables. Analogously to Principal Components, these components are uncorrelated and sequentially best approximate the dataset.
openaire   +2 more sources