Principal Component
Euler principal component analysis
... In pattern recognition, Principal Component Analysis (PCA) is perhaps the most classical tool for dimensionality reduc- tion and feature extraction. It is widely utilized in a great va- riety of ...
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Face Recognition Using Principal Component Analysis
... on Principal Component Analysis based and LDA and then on Neural Network used combining Neural Network and Genetic Algorithm and some are on combining Principal Component Analysis and Genetic ...
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Application of Principal Component Analysis in Reservoir Evaluation
... When making a comprehensive evaluation of reservoir or different blocks, we should select those representatives, comparability and practical parameters on the basis of the influence of individual parameters on the ...
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Principal Component Analysis in ECG Signal Processing
... the principal components. Calculation of the principal components from successive beats followed by spectral analysis of the resulting series of principal compo- nents is a powerful approach to ...
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Face Recognition Using Principal Component Analysis
... quite satisfactory [6]. Janarbek Matai et al. presented FPGA-based Real-Time Face Recognition System in which they design full face detection and recognition system on FPGA vertex 5. They used haar data for detection and ...
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Dual Principal Component Pursuit
... sphere, referred to as the Dual Principal Component Pursuit (DPCP) problem. We provide theoreti- cal guarantees under which every global solution of the DPCP problem is a vector orthogonal to the linear ...
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A Simplified Approach for Interpreting Principal Component Images
... Principal component transform (PCT), also known as eigenvector transformation, Hotelling transformation, Karhunen-Loève (K-L) transformation, eigenvalue-eigen- vector decomposition, is a standard and highly ...
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Convex Formulations for Fair Principal Component Analysis
... Though there is a growing literature on fairness for supervised learning, incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal ...
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Online Tensor Robust Principal Component Analysis
... Since these 2-dimensional methods were developed, our understanding of tensors and multidimensional data has grown. In particular, the development of a tensor multipli- cation known as the ‘t-product’ that generalises ...
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Association tests based on the principal component analysis
... Haplotypes are composed of specific combinations of alleles at the several loci on the same chromosome. Because haplotypes incorporate linkage disequilibrium (LD) information from multiple loci, haplotype-based ...
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Principal Component Analysis of Volatility Smiles and Skews
... Several principal component models of volatility smiles and skews have been based on daily changes in implied volatilities, by strike and/or by moneyness. Derman and Kamal (1997) analyze S&P500 and ...
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Principal Component Analysis of the Volatility Smiles and Skews
... • Fengler, M., W. Hardle and C. Villa (2000) "The Dynamics of Implied Volatilities: A Common Principal Component Approach" Preliminary version (September 2000) available from ...
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MFPCA: Multiscale Functional Principal Component Analysis
... Functional principal component analysis (FPCA) is a key tool for performing dimension reduction on functional data that features infinite dimensionality and emerges in many machine learning applications ...
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II. THE CLASSICAL PRINCIPAL COMPONENT ANALYSIS (PCA)
... Tr Σ is not limited to small or low dimension data set and to non-singular covariance matrix. VV can be used efficiently for very large and high dimension data sets or even for singular covariance matrix. The MVV ...
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Identification of critical parameters in sintering process through integrated grey relation analysis principal component analysis and response surface method
... The principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics so that their relative importance can be properly and objectively ...
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Robust Recognition using L1-Principal Component Analysis
... Numerous extensions to eigenfaces have been proposed to overcome its limitations. Modular eigenfaces is one such extension that is more robust to occlusions, variations in illumination and facial expression. Modular ...
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A Review of Constrained Principal Component Analysis (CPCA) with Application on Bootstrap
... The previous example indicated that LM is an important statistical development in the last fifty years following GLM, PCA and CPCA in the last thirty years. This paper introduced a series of papers prepared within the ...
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Dimensionality Reduction of Image Feature Based on Mean Principal Component Analysis
... The principal component analysis is based on the covariance ...the principal components will vary between dimension and the change of the order of ...
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Principal component analyses for tree structured objects
... of Principal Component lines in binary tree space, but due to the lack of a solution to the resulting optimization problems, only limited toy examples (three and four node trees, which thus allowed manual ...
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Optimal Solutions for Sparse Principal Component Analysis
... Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This ...
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