# PRINCIPAL COMPONENT ANALYSIS

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

** Principal component analysis **

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Reliability is the probability that a piece of equipment (component, subsystem or system) successfully performs its intended function for a given period of time under specified (design) conditions (Martz and Waller). Failure means that an item does not perform its

** Asymptotic theory for principal component analysis **

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The asymptotic distribution of the characteristic roots and (normalized) vectors of a sample covariance matrix is given when the observations are from a multivariate normal distribution Whose covariance matrix has characteristic roots of arbitrary multiplicity. The elements of

**Singular value decomposition and principal component analysis **

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One of the challenges of bioinformatics is to develop effective ways to analyze global gene expression data. A rigorous approach to gene expression analysis must involve an up-front characterization of the structure of the data. Singular value decomposition (SVD) and

** Principal component analysis for alteration mapping**

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Reducing the number of image bands input for principal component analysis (PCA) ensures that certain materials will not be mapped and increases the likelihood that others will be unequivocally mapped into only one of the principal component images. In arid terrain, PCA

** Size and shape variation in the painted turtle. A principal component analysis **

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The concepts of size and shape are fundamental to the analysis of variation in living organisms. Ahd yet, as noted by Simpson, Roe and Lewontin (1960), there is at present no general agreement on practical definitions of size and shape. The adoption of a generally

** Structured sparse principal component analysis **

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We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This structured sparse PCA is based on a structured regularization recently

** Principal component analysis for hyperspectral image classification**

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The availability of hyperspectral images expands the capability of using image classification to study detailed characteristics of objects, but at a cost of having to deal with huge data sets. This work studies the use of the principal component analysis as a preprocessing technique

** Sparse kernel principal component analysis **

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AbstractKernel principal component analysis (PCA) is an elegant nonlinear generalisation of the popular linear data analysis method, where a kernel function implicitly defines a nonlinear transformation into a feature space wherein standard PCA is performed

** Applications of principal component analysis to horticultural research**

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Horticultural researchers often must measure complex traits, such as vigor, reproductive performance, morphology, or adaptability, and develop relationships with treatments or associated variables. Complex traits, however, are a composite of individual traits that often

** Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis **

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A challenge encountered with Landsat Thematic Mapper (TM) data, which includes data from six reflective spectral bands~ IS dlsplaymg as much information as possible in a three- image set for color compositing or digital analySIS . PnnClpal component analysis (PCA)

** Principal component analysis and factor analysis **

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Occasionally there are lot of variables in the data which create problem for analyzing data and coming down to some inferences. Hence, it is often necessary to reduce dimension of the dataset to get appropriate, meaningful and valid results. Two widely employed data

**Principal component analysis with missing data and its application to polyhedral object modeling**

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ABSTRACT it> Abstract /it> Observation-based object modeling often requires integration of shape descriptions from different views. In current conventional methods, to sequentially merge multiple views, an accurate description of each surface patch has to be

** An overview of principal component analysis **

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The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool

** A generalized linear model for principal component analysis of binary data.**

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Fitting the Un in logit/normit factor is harder than in LPCA. It requires an additional variational approximation and iterative process. Model fitting improves the lower bound on the log-likelihood. not necessarily the log-likelihood itself. In contrast, ALS for LPCA

** Principal component analysis **

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Principal Component Analysis (PCA) is the general name for a technique which uses sophisticated underlying mathematical principles to transforms a number of possibly correlated variables into a smaller number of variables called principal components. The

** Robust principal component analysis with complex noise**

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The research on robust principal component analysis (RPCA) has been attracting much attention recently. The original RPCA model assumes sparse noise, and use the L1-norm to characterize the error term. In practice, however, the noise is much more complex and it is

** Real-time recognition of hand alphabet gestures using principal component analysis **

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This work presents a design for a human computer interface capable of recognizing 25 gestures from the international hand alphabet in real-time. Principal Component Analysis (PCA) is used to extract features from images of gestures. The features represent gesture

** Sparse probabilistic principal component analysis **

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Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it is not easy to interpret which of the original features are important based on the principal components. Recent methods improve interpretability by sparsifying PCA through

** Directional field computation for fingerprints based on the principal component analysis of local gradients**

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This paper discusses a new method, based on the principal component analysis , to estimate the directional field of fingerprints. The method not only computes the direction in any pixel location, but its coherence as well. It will be proven that this method provides exactly the

** On the convergence of eigenspaces in kernel principal component analysis **

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This paper presents a non-asymptotic statistical analysis of Kernel-PCA with a focus different from the one proposed in previous work on this topic. Here instead of considering the reconstruction error of KPCA we are interested in approximation error bounds for the

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