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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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Geodesic PCA versus Log-PCA of Histograms in the Wasserstein Space

SIAM Journal on Scientific Computing, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elsa Cazelles   +4 more
openaire   +2 more sources

Subcutaneous-PCA

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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Robust PCAs and PCA Using Generalized Mean

2017
In 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
openaire   +1 more source

Forecasting crude oil prices: A scaled PCA approach

Energy Economics, 2021
Yudong Wang, Yaojie Zhang, Mengxi He
exaly  

When is a PCA a PCA?

Journal of Philosophy of Education, 1982
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PCA

American Journal of Nursing, 1996
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PCA

Anesthesiology, 1989
B Ginsberg, K M Gil, M Muir, D Sykes
openaire   +1 more source

Scaled PCA: A New Approach to Dimension Reduction

Management Science, 2022
Guofu Zhou, Fuwei Jiang, Dashan Huang
exaly  

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