Results 81 to 90 of about 232,203 (264)

Robust sparse smooth principal component analysis for face reconstruction and recognition.

PLoS ONE
Existing Robust Sparse Principal Component Analysis (RSPCA) does not incorporate the two-dimensional spatial structure information of images. To address this issue, we introduce a smooth constraint that characterizes the spatial structure information of ...
Jing Wang, Xiao Xie, Li Zhang, Jian Li, Hao Cai, Yan Feng   +5 more
doaj   +1 more source

Functional principal component analysis and sparse-group LASSO to identify associations between biomarker trajectories and mortality among hospitalized SARS-CoV-2 infected individuals

BMC Medical Research Methodology, 2023
Background A substantial body of clinical research involving individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evaluated the association between in-hospital biomarkers and severe SARS-CoV-2 outcomes, including ...
Tingyi Cao, Harrison T. Reeder, Andrea S. Foulkes   +2 more
doaj   +1 more source

Patterns of Postictal Abnormalities in Relation to Status Epilepticus in Adults

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Abnormalities on peri‐ictal diffusion‐weighted magnetic resonance imaging (DWI‐PMAs) are well‐established for patients with status epilepticus (SE), but knowledge on patterns of DWI‐PMAs and their prognostic impact is sparse. Methods This systematic review and individual participant data meta‐analysis included observational studies ...
Andrea Enerstad Bolle, Silvana Sarria‐Estrada, Thomas Bonduelle, Bertram Daniel Dynesen, Manuel Toledo, Zhu Chung Che, Kjell Heuser, Anastasios Chatzikonstantinou, Jérôme Aupy, Udaya Seneviratne, Estevo Santamarina Perez, Christoph Patrick Beier   +11 more
wiley   +1 more source

Information-theoretically Optimal Sparse PCA

, 2014
Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of a data matrix with additional sparsity constraints on the obtained representation. We consider two probabilistic formulations
Deshpande, Yash, Montanari, Andrea
core   +1 more source

Development of a Prediction Model for Progression Risk in High‐Grade Gliomas Based on Habitat Radiomics and Pathomics

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu, Yuxi Gong, Weilin Xu, Xingjian Sun, Gefei Jiang, Lei Qiu, Kexin Shi, Mengxing Wu, Yinjiao Fei, Jinling Yuan, Jinyan Luo, Yurong Li, Yuandong Cao, Minhong Pan, Shu Zhou   +14 more
wiley   +1 more source

Sparse Principal Component Analysis and Adaptive Multigraph Learning for Hyperspectral Band Selection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Band selection (BS) is an effective dimensionality reduction technique for hyperspectral images. Although many relevant methods have been proposed, they often only focus on the bandwise information and the correlation between the bands, and few of them ...
Wenxian Zhang, Aihong Yuan, Jinglei Tang, Xuelong Li   +3 more
doaj   +1 more source

Clustering Algorithm for High-Dimensional Data Under New Dimensionality Reduc-tion Criteria

Jisuanji kexue yu tansuo, 2020
In order to solve the problem that principal component analysis (PCA) algorithm can??t deal with the reduction of clustering accuracy after high dimensional data reduction, a new attribute space concept is proposed.
WAN Jing, WU Fan, HE Yunbin, LI Song
doaj   +1 more source

Penalized Orthogonal Iteration for Sparse Estimation of Generalized Eigenvalue Problem

, 2017
We propose a new algorithm for sparse estimation of eigenvectors in generalized eigenvalue problems (GEP). The GEP arises in a number of modern data-analytic situations and statistical methods, including principal component analysis (PCA), multiclass ...
Anant Agrawal (3953690), Andrea Lozzi (3953780), Cristin G. Welle (3953777), Daniel X. Hammer (3952427), Erkinay Abliz (3953774), Noah Greenbaum (3953768), Victor Krauthamer (3953771)   +6 more
core   +3 more sources

Brainstem and Cerebellar Volume Loss and Associated Clinical Features in Progressive Supranuclear Palsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Introduction Progressive Supranuclear Palsy (PSP) is a neurodegenerative ‘tauopathy’ with predominating pathology in the basal ganglia and midbrain. Caudal tau spread frequently implicates the cerebellum; however, the pattern of atrophy remains equivocal.
Chloe Spiegel, Timothy P. Siejka, Cassandra Marotta, Josh J. Y. Lee, Kelly Bertram, Terence J. O'Brien, Meng Law, Lucy Vivash, Ian H. Harding   +8 more
wiley   +1 more source

Image restoration based on adaptive switching between synthesis and analysis sparse regularisation

The Journal of Engineering, 2019
Both synthesis and analysis sparse regularisation have been successfully applied to solve various inverse vision problems. The authors find an optimisation model to combine the power of the dual sparse prior models for image restoration.
Huahua Chen, Jiling Xue, Yu Lu, Chunsheng Guo   +3 more
doaj   +1 more source