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SIAM Journal on Optimization, 2023
Abstract. We propose an inverted approach to the Sparse Principal Component Analysis (SPCA) problem. Most previous research efforts focused on solving the problem of maximizing the variance subject to sparsity constraints or penalizing lack of sparsity.
Fatih S. Aktas, Mustafa Ç. Pinar
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Abstract. We propose an inverted approach to the Sparse Principal Component Analysis (SPCA) problem. Most previous research efforts focused on solving the problem of maximizing the variance subject to sparsity constraints or penalizing lack of sparsity.
Fatih S. Aktas, Mustafa Ç. Pinar
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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 ...
Hongchuan Yu, Mohammed Bennamoun
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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 ...
Hongchuan Yu, Mohammed Bennamoun
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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 +5 more
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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 +5 more
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Two-Dimensional Quaternion PCA and Sparse PCA
IEEE Transactions on Neural Networks and Learning Systems, 2019Benefited 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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2007
Histograms are data objects that are commonly used to characterize media objects like image, video, audio etc. Symbolic Data Analysis (SDA) is a field which deals with extracting knowledge and relationship from such complex data objects. The current research scenario of SDA has contributions related to dimensionality reduction of interval kind data ...
P. Nagabhushan, R. Pradeep Kumar
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Histograms are data objects that are commonly used to characterize media objects like image, video, audio etc. Symbolic Data Analysis (SDA) is a field which deals with extracting knowledge and relationship from such complex data objects. The current research scenario of SDA has contributions related to dimensionality reduction of interval kind data ...
P. Nagabhushan, R. Pradeep Kumar
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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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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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Geodesic PCA versus Log-PCA of Histograms in the Wasserstein Space
SIAM Journal on Scientific Computing, 2018This paper is concerned with the statistical analysis of datasets whose elements are random histograms. For the purpose of learning principal modes of variation from such data, we consider the issue of computing the principal component analysis (PCA) of histograms with respect to the 2-Wasserstein distance between probability measures.
Elsa Cazelles +4 more
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The Clinical Journal of Pain, 1990
Patients (n = 120) undergoing major orthopedic (e.g., total hip replacement), urologic (e.g., radical prostatectomy), or gynecologic (e.g., total abdominal hysterectomy) procedures were randomly assigned to receive either morphine or oxymorphone postoperatively using a patient-controlled analgesic (PCA) delivery system.
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Patients (n = 120) undergoing major orthopedic (e.g., total hip replacement), urologic (e.g., radical prostatectomy), or gynecologic (e.g., total abdominal hysterectomy) procedures were randomly assigned to receive either morphine or oxymorphone postoperatively using a patient-controlled analgesic (PCA) delivery system.
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Robust PCAs and PCA Using Generalized Mean
2017In this chapter, a robust principal component analysis (PCA) is described, which can overcome the problem that PCA is prone to outliers included in training set. Different from the other alternatives which commonly replace \(L_{2}\)-norm by other distance measures, our method alleviates the negative effect of outliers using the characteristic of the ...
Jiyong Oh, Nojun Kwak
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Tutorial on PCA and Approximate PCA and Approximate Kernel PCA
Artificial Intelligence Review, 2023Sanparith Marukatat
exaly