component analysis
Improvement on the vanishing component analysis by grouping strategy
... Vanishing component analysis (VCA) method, as an important method integrating commutative algebra with machine learning, utilizes the polynomial of vanishing component to extract the features of ...
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Dimensionality Reduction of Image Feature Based on Mean Principal Component Analysis
... At present, the feature reduction methods can be divided into two categories, linear methods and nonlinear methods [2]. The linear representation methods include Principal Component Analysis (PCA) and ...
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Comparison of Iris Recognition Using Gabor Wavelet, Principal Component Analysis and Independent Component Analysis
... An attempt has been made in this work to compare performance of Iris Recognition based on feature extraction using Gabor wavelet, Principal Component Analysis(PCA) and Independent Component ...
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Bilinear Discriminant Component Analysis
... Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data ...Discriminant Component Analysis (BDCA), is derived ...
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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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Performance Analysis of Independent Component Analysis based on Blind Source Separation for extraction of Atrial Activity
... Independent Component analysis (ICA) is a processing process which performs blind source separation of independent statistical sources components by assuming linear mixing of sources with sensors; this ...
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Voltage Flicker Analysis Based on Improved Independent Component Analysis
... independent component analysis uses Newton method to optimize its cost function, so it always falls into local optimal ...independent component analysis, this paper uses artificial bee colony ...
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Epilepsy EEG classification using morphological component analysis
... Independent component analysis; IMF: Intrinsic mode function; KNN: K nearest neighbor; ...Morphological component analysis; MLP: Multilayer perceptron; NN: Nearest neighbor; PC: Principal ...
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A Review of Constrained Principal Component Analysis (CPCA) with Application on Bootstrap
... regression analysis, where it was considered an important statistical development of the last fifty years, following general linear model (GLM), principal component analysis (PCA) and constrained ...
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Component retention in principal component analysis with application to cDNA microarray data
... Data reduction is frequently instrumental in revealing mathematical structure. The challenge is to balance the accuracy (or fit) of the model with ease of analysis and the potential loss of information. To ...
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Lithological Discrimination of Anorthosite using ASTER data in Oddanchatram Area, Dindigul district, Tamil Nadu, India
... Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Minimum Noise Fraction (MNF), Pixel Purity Index (PPI) and n- Dimensional Visualization for better lithology ...
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A Wireless Signal Denoising Model for Human Activity Recognition
... The contributions of our works are as follows. Firstly, we utilize traditional average filter algorithms to reduce noise of wireless signals and analyze their differences, which is effective to remove the environment ...
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Comparison of Neural Networks and Support Vector Machines using PCA and ICA for Feature Reduction
... Principal Component Analysis (PCA) and Independent Component Analysis (ICA) in web news classification is to perform feature separation and ...
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Performance Comparison of EM, MEM, CTM, PCA, ICA, Entropy and MI for Photoplethysmography Signals
... Independent Component Analysis describes a model for multivariate data describing large database of ...Independent Component Analysis is used to uncover the independent source signals from a ...
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ENERGY AWARE GRID RESOURCE ALLOCATION BY USING A NOVEL NEGOTIATION MODEL
... The face recognition method is classified into two: Holistic matching method and local matching method. The entire face image is considered for the holistic matching method. This is based on Principle component ...
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A biologically inspired vision-based approach for detecting multiple moving objects in complex outdoor scenes
... independent component analysis (ICA) with principal component analysis (PCA) is proposed in this paper for multiple moving objects detection in complex scenes—a major real-time challenge as ...
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Network Intrusion detection by using PCA via SMO-SVM
... One solution to this is the use of network intrusion detection systems (NIDS), which detect attacks by observing various network activities. It is therefore crucial that such systems are accurate in identifying attacks, ...
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Electrocardiogram Beat Classification using Discrete Wavelet Transform, Higher Order Statistics and Multivariate Analysis
... Wavelet Transform is appropriate to analyze non stationary signals. The feature vectors from the ECG data set created by extracting the Wavelet transform , linear dimensionality reduction technique PCA and Higher order ...
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Criminal Identification and Alert System
... Our approach treats face recognition as a two-dimensional recognition problem. In this scheme face recognition is done by Principal Component Analysis (PCA).Facial recognition technique is newly developed ...
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A STUDY OF FACE RECOGNITION TECHNIQUES
... Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA), Independent Component Analysis (ICA), Elastic Bunch Graph Matching (EBGM), Line Edge Map (LEM), Support Vector ...
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