two-dimensional principal component analysis
Face Recognition Using Principal Component Analysis
... intrinsically two dimensional (2D) recognition problem rather than requiring recovery of 3D geometry, proceeds advantage of the fact that faces are normally upright and thus may be described by a small set ...
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Kernal principal component analysis of the ear morphology
... The outer ear is an intricate shape and examining the non-linear variations in the ear morphology between listeners is a challenging task. We consider ear shape diffeomorphisms as belonging to a Riemanian space. In this ...
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Bilinear probabilistic principal component analysis
... developed two maximum likelihood estimation algorithms for BPPCA, one is based on CM while the other is based on ...are principal subspaces of the column and row covariance matrices (up to scaling and ...
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← Return to Article Details CHARACTERIZATION OF TEMPORAL BIODEGRADATION OF RADIATA PINE BY GLOEOPHYLLUM TRABEUM THROUGH PRINCIPAL COMPONENT ANALYSIS-BASED TWO-DIMENSIONAL CORRELATION FTIR SPECTROSCOPY
... Fig. 1 shows the reconstructed FTIR spectra of decayed wood measured during the degradation process over a 16-week period. The reconstructed data matrix from the three principal components was used instead of the ...
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Principal Component Analysis with SVM for Disease Diagnosis
... a two-class classifier that can produce a hyperplane for classifying two data ...for two-class linearly separable issue in an n-dimensional feature space is given as in ...
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Principal Component Analysis to Detect Anomaly in High Dimensional Data using Cluster
... To find influence data instances angle based method uses angle parameter. That means it finds angle between objective instances. If considered objective data instance is influence then it shows small angle variation. ...
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A General Framework for Consistency of Principal Component Analysis
... We now provide the detailed proof for Theorem 1. To save space, the proofs for Theorem 2 and the corresponding corollaries of the two theorems (which are often similar, and simpler) are provided in the supplement ...
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Torus principal component analysis with applications to RNA structure
... in two previously proposed PCA methods: Unlike tangent space PCA, our torus-PCA features structure fidelity by honoring the cyclic topology of the data space, and, unlike geodesic PCA, produces non-winding, ...
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A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis
... high-dimensional principal component analysis (PCA) suffers from variance infla- tion and lack of ...in two directions: First, we propose a computationally less intensive approximate ...
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High dimensional Data Classification Based on Principal Component Analysis Dimension Reduction and Improved BP Algorithm
... disaster, principal component analysis (PCA) is applied to reduce dimension of high-dimensional data firstly, and then BP neural network is applied to ...with two times adaptive adjust ...
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Group-Wise Principal Component Analysis for Exploratory Data Analysis
... factor analysis (FA) has been ...a two-step procedure is typically followed (Jolliffe 2002; Jackson ...each component is perceived as a simple structure (Timmer- man, Kiers, and Smilde ...
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Improving Probabilistic Latent Semantic Analysis with Principal Component Analysis
... Semantic Analysis (PLSA) models have been shown to pro- vide a better model for capturing poly- semy and synonymy than Latent Seman- tic Analysis ...LSA analysis to initialize a PLSA ...
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Component retention in principal component analysis with application to cDNA microarray data
... of analysis and the potential loss of ...to principal components derived from real data may not be substantially greater than that derived from randomly generated ...
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Multilevel approximate robust principal component analysis
... lower dimensional counterparts for the computationally expensive parts of each algorithm and use their solutions for finding approximate solutions for the original fine level ...
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Online Tensor Robust Principal Component Analysis
... In many fields of modern data analysis, the observed data are not necessarily the prime objects of interest. The computer steering a driverless car should pay more attention to objects moving across the foreground ...
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Application of Principal Component Analysis in Reservoir Evaluation
... cluster analysis is adopted to reasonably optimize the geologic and development parameters that can reflect the reservoir ...of principal component analysis, we can determine the weight of ...
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TUCKALS3. Three-mode principal component analysis
... Several centrings can be performed in the program, primarily on frontal slices of the three-way matrix, such as centring rows, columns or frontal slices, and standardization of frontal s[r] ...
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A Principal Component Analysis based Recognition of Facial Expression
... Facial expression analysis deals with visually recognizing and analyzing different facial motions and facial feature changes. The facial expression recognition system consists of four steps (Figure 1). First is ...
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Bridge Monitoring with Harmonic Excitation and Principal Component Analysis
... Bungard, & De Roeck, 2012). It consists in an eccentric mass exciter (force amplitudes above 10 kN for f > 4 Hz) and an electromagnetic exciter with a feedback control loop (force amplitudes up to 2.7 kN) ...
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Generalized Power Method for Sparse Principal Component Analysis
... four new algorithms for computing sparse principal components of a matrix A ∈ R p × n . Second, our algorithms appear to be faster if either the objective function or the feasible set are strongly convex, which ...
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