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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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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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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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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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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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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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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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