Results 291 to 300 of about 896,372 (322)

Prediagnosis Prostate-Specific Antigen Testing History in Patients With Incident Prostate Cancer.

JAMA Netw Open
Guittet L, Denis P, Tuppin P, Fiard G, Mathieu R, De Bels F, Le Bihan C, Rachas A.   +7 more
europepmc   +1 more source

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PCA Sparsified

SIAM Journal on Optimization, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fatih S. Aktaş, Mustafa Ç. Pinar
openaire   +1 more source

1D-PCA, 2D-PCA to nD-PCA

18th International Conference on Pattern Recognition (ICPR'06), 2006
In this paper, we first briefly reintroduce the 1D and 2D forms of the classical principal component analysis (PCA). Then, the PCA technique is further developed and extended to an arbitrary n-dimensional space. Analogous to 1D- and 2D-PCA, the new nD-PCA is applied directly to n-order tensors (n ges 3) rather than 1-order tensors (1D vectors) and 2 ...
null Hongchuan Yu, M. Bennamoun
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Two-Dimensional Quaternion PCA and Sparse PCA

IEEE Transactions on Neural Networks and Learning Systems, 2019
Benefited from quaternion representation that is able to encode the cross-channel correlation of color images, quaternion principle component analysis (QPCA) was proposed to extract features from color images while reducing the feature dimension. A quaternion covariance matrix (QCM) of input samples was constructed, and its eigenvectors were derived to
Xiaolin Xiao, Yicong Zhou
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Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction

2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020
Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction. In this study, we search optimal feature extraction algorithm by evaluating the performance of different feature ...
Atiqur Rehman, Aurangzeb Khan, Muhammad Akhtar Ali, Muhammad Umair Khan, Shafqat Ullah Khan, Liaqat Ali   +5 more
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PCA and kernel PCA

2014
Introduction Two primary techniques for dimension-reducing feature extraction are subspace projection and feature selection . This chapter will explore the key subspace projection approaches, i.e. PCA and KPCA. (i) Section 3.2 provides motivations for dimension reduction by pointing out (1) the potential adverse effect of large feature ...
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Local PCA algorithms

IEEE Transactions on Neural Networks, 2000
Within the last years various principal component analysis (PCA) algorithms have been proposed. In this paper we use a general framework to describe those PCA algorithms which are based on Hebbian learning. For an important subset of these algorithms, the local algorithms, we fully describe their equilibria, where all lateral connections are set to ...
A, Weingessel, K, Hornik
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Comparison of PCA and 2D-PCA on Indian Faces

2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014), 2014
Face recognition is an extensively researched topic by researchers from diverse disciplines. Several unsupervised statistical feature extraction methods have been used in face recognition, out of these in this paper a comparison of the PCA(eigenfaces) and 2D-PCA approaches on Indian Faces has been presented.
Sekhar Rajendran, Amit Kaul, Ravinder Nath, A. S. Arora, Sushil Chauhan   +4 more
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