نتایج جستجو برای: principal component analysis pca

تعداد نتایج: 3339272  

2007
Marian Stewart Bartlett Terrence J. Sejnowski

Methods for obtaining representations of face images based on independent component analysis (ICA) are presented. A global ICA representation is compared to a global representation based on principal component analysis (PCA) for recognizing faces across changes in lighting and changes in pose. For each set of face images, a set of statistically independent source images was found through an uns...

1998
Christopher M. Bishop

The technique of principal component analysis (PCA) has recently been expressed as the maximum likelihood solution for a generative latent variable model. In this paper we use this probabilistic reformulation as the basis for a Bayesian treatment of PCA. Our key result is that effective dimensionality of the latent space (equivalent to the number of retained principal components) can be determi...

2008
Haisheng Lin Ognjen Marjanovic Barry Lennox

This paper focuses on the application and comparison of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) using two generic artificially created datasets. PCA and ICA are assessed in terms of their abilities to infer reference spectra and to estimate relative concentrations of the constituent compounds present in the analysed samples. The results show that ICA outperfo...

شیرزاد, الهام, عزیزیان, سعیده, عنبریان, مهرداد, نقیبی, سید احسان, یوسفی, محمد,

Objective: Despite the importance of identifying people susceptible to sports, there is little documentation and studies related to karate talent identification.The purpose of this study was principal component analysis of anthropometric and biomechanical variables in adolescent elite karateka athletes. Methods: Subjects divided to adolescent elite karateka athletes (n = 19) and non-karateka a...

2017
Qi-Shi Du Shu-Qing Wang Neng-Zhong Xie Qing-Yan Wang Ri-Bo Huang Kuo-Chen Chou

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural frag...

1997
Michael E. Tipping Christopher M. Bishop

Principal component analysis (PCA) is a ubiquitous technique for data analysis but one whose effective application is restricted by its global linear character. While global nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data nonlinearity by a mixture of local PCA models. However, existing techniques are limited by the absence of a probabilistic formalism wi...

Journal: :Neural computation 1999
M E Tipping C M Bishop

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing, and visualizing data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a pro...

2014
D. Garcia-Alvarez M. J. Fuente

This article studies and describes a monitoring, fault detection, and diagnosis technique based on the unfolded PCA (UPCA) approach and its application to a reverse osmosis desalination plant. The UPCA approach is normally applied to batch processes, but in this case, the UPCA approach is applied to a continuous process, which does not present a strict steady state. The classical principal comp...

Journal: :Journal of Machine Learning Research 2011
Trine Julie Abrahamsen Lars Kai Hansen

Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we...

Journal: :Journal of chemical information and modeling 2010
Indrek Tulp Dimitar A. Dobchev Alan R. Katritzky William E. Acree Uko Maran

Principal component analysis (PCA) of a large data matrix (153 solvents x 396 solutes) for Ostwald solubility coefficients (log L) resulted in a two-component model covering 98.6% of the variability. Analysis of the principal components exposed the structural characteristics of solutes and solvents that codify interactions which determine the behavior of a chemical in the surrounding media. The...

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