نتایج جستجو برای: principal constituents analysis pca

تعداد نتایج: 2930818  

2004
Pengcheng Xi Tao Xu

ABSTRACT Principal Component Analysis (PCA) is a basis transformation to diagonalize an estimate of the covariance matrix of input data and, the new coordinates in the Eigenvector basis are called principal components. Since Kernel PCA is just a PCA in feature space F , the projection of an image in input space can be reconstructed from its principal components in feature space. This enables us...

Journal: :Communications in Statistics - Simulation and Computation 2016
Joost C. F. de Winter Dimitra Dodou

Common factor analysis (CFA) and principal component analysis (PCA) are widely used multivariate techniques. Using simulations, we compared CFA with PCA loadings for distortions of a perfect cluster configuration. Results showed that nonzero PCA loadings were higher and more stable than nonzero CFA loadings. Compared to CFA loadings, PCA loadings correlated weakly with the true factor loadings ...

Journal: :International Journal of Computer Science and Information Technology 2022

Some students in the Computer Science and related majors excel very well programming-related assignments, but not equally theoretical assignments (that are programming-based) vice-versa. We refer to this as "Theory vs. Programming Disparity (TPD)". In paper, we propose a spectral bipartivity analysis-based approach quantify TPD metric for any student course based on percentage scores (considere...

2012
Rashish Tandon

Principal Component Analysis (PCA) is a frequently used tool to analyse, visualize and reduce the dimensionality of data occurring in a variety of fields in science and engineering. Given a data matrix X ∈ Rn×p (where n is the number of points and p is the dimensionality), PCA finds a set of d(≪ p) orthonormal vectors V = {v1, v2, . . . , vd} in R such that the span(V ) explains the maximum amo...

2015
CHAO GAO HARRISON H. ZHOU H. H. ZHOU

Principal component analysis (PCA) is possibly one of the most widely used statistical tools to recover a low-rank structure of the data. In the highdimensional settings, the leading eigenvector of the sample covariance can be nearly orthogonal to the true eigenvector. A sparse structure is then commonly assumed along with a low rank structure. Recently, minimax estimation rates of sparse PCA w...

2002
Yiu-ming Cheung

The recent paper (Cheung 2001) has studied the blind identification of Gaussian source process through a general temporal independent component analysis (ICA) approach named dual autoregressive modelling. It is actually a temporal extension of the classical principal component analysis without considering the principal order of the components. In this paper, we will further show the identifiabl...

2004
Hashem Tamimi Andreas Zell

The aim of this article is to present the potential of Kernel Principal Component Analysis (Kernel PCA) in the field of vision based robot localization. Using Kernel PCA we can extract features from the visual scene of a mobile robot. The analysis is applied only to local features so as to guarantee better computational performance as well as translation invariance. Compared with the classical ...

Journal: :Molecules 2017
Jun Liang Hui-Min Sun Tian-Long Wang

The Shuang-Huang-Lian (SHL) oral liquid is a combined herbal prescription used in the treatment of acute upper respiratory tract infection, acute bronchitis and pneumonia. Multiple constituents are considered to be responsible for the therapeutic effects of SHL. However, the quantitation of the multi-components from multiple classes is still unsatisfactory because of the high complexity of cons...

2004
Jacob D. McDonald Ingvar Eide JeanClare Seagrave Barbara Zielinska Kevin Whitney Douglas R. Lawson Joe L. Mauderly

In this study we investigated the statistical relationship between particle and semivolatile organic chemical constituents in gasoline and diesel vehicle exhaust samples, and toxicity as measured by inflammation and tissue damage in rat lungs and mutagenicity in bacteria. Exhaust samples were collected from "normal" and "high-emitting" gasoline and diesel light-duty vehicles. We employed a comb...

2006
Seungjin Choi

In this letter we present a coupled Helmholtz machine for principal component analysis (PCA), where sub-machines are related through sharing some latent variables and associated weights. Then, we present a wake-sleep PCA algorithm for training the coupled Helmholtz machine, showing that the algorithm iteratively determines principal eigenvectors of a data covariance matrix without any rotationa...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید