• Title/Summary/Keyword: Robust 퍼지 PCA

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An Improved Robust Fuzzy Principal Component Analysis (잡음 민감성이 개선된 퍼지 주성분 분석)

  • Heo, Gyeong-Yong;Woo, Young-Woon;Kim, Seong-Hoon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.5
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    • pp.1093-1102
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    • 2010
  • Principal component analysis (PCA) is a well-known method for dimension reduction while maintaining most of the variation in data. Although PCA has been applied to many areas successfully, it is sensitive to outliers. Several variants of PCA have been proposed to resolve the problem and, among the variants, robust fuzzy PCA (RF-PCA) demonstrated promising results. RF-PCA uses fuzzy memberships to reduce the noise sensitivity. However, there are also problems in RF-PCA and the convergence property is one of them. RF-PCA uses two different objective functions to update memberships and principal components, which is the main reason of the lack of convergence property. The difference between two functions also slows the convergence and deteriorates the solutions of RF-PCA. In this paper, a variant of RF-PCA, called RF-PCA2, is proposed. RF-PCA2 uses an integrated objective function both for memberships and principal components. By using alternating optimization, RF-PCA2 is guaranteed to converge on a local optimum. Furthermore, RF-PCA2 converges faster than RF-PCA and the solutions found are more similar to the desired solutions than those of RF-PCA. Experimental results also support this.

A Variant of Improved Robust Fuzzy PCA (잡음 민감성이 개선된 변형 퍼지 주성분 분석 기법)

  • Kim, Seong-Hoon;Heo, Gyeong-Yong;Woo, Young-Woon
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.2
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    • pp.25-31
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    • 2011
  • Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction. Although PCA has been applied in many areas successfully, it is sensitive to outliers due to the use of sum-square-error. Several variants of PCA have been proposed to resolve the noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA2, however, still can fall into a local optimum due to equal initial membership values for all data points. Another reason comes from the fact that RF-PCA2 is based on sum-square-error although fuzzy memberships are incorporated. In this paper, a variant of RF-PCA2 called RF-PCA3 is proposed. The proposed algorithm is based on the objective function of RF-PCA2. RF-PCA3 augments RF-PCA2 with the objective function of PCA and initial membership calculation using data distribution, which make RF-PCA3 to have more chance to converge on a better solution than that of RF-PCA2. RF-PCA3 outperforms RF-PCA2, which is demonstrated by experimental results.

A Non-linear Variant of Improved Robust Fuzzy PCA (잡음 민감성이 향상된 주성분 분석 기법의 비선형 변형)

  • Heo, Gyeong-Yong;Seo, Jin-Seok;Lee, Im-Geun
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.4
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    • pp.15-22
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    • 2011
  • Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction while maintaining most of the variation in data. Although PCA has been applied in many areas successfully, it is sensitive to outliers and only valid for Gaussian distributions. Several variants of PCA have been proposed to resolve noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA, however, is still a linear algorithm that cannot accommodate non-Gaussian distributions. In this paper, a non-linear algorithm that combines RF-PCA2 and kernel PCA (K-PCA), called improved robust kernel fuzzy PCA (RKF-PCA2), is introduced. The kernel methods make it to accommodate non-Gaussian distributions. RKF-PCA2 inherits noise robustness from RF-PCA2 and non-linearity from K-PCA. RKF-PCA2 outperforms previous methods in handling non-Gaussian distributions in a noise robust way. Experimental results also support this.

Design of Robust Face Recognition System with Illumination Variation Realized with the Aid of CT Preprocessing Method (CT 전처리 기법을 이용하여 조명변화에 강인한 얼굴인식 시스템 설계)

  • Jin, Yong-Tak;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of the Korean Institute of Intelligent Systems
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    • v.25 no.1
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    • pp.91-96
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    • 2015
  • In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

Design of ASM-based Face Recognition System Using (2D)2 Hybird Preprocessing Algorithm (ASM기반 (2D)2 하이브리드 전처리 알고리즘을 이용한 얼굴인식 시스템 설계)

  • Kim, Hyun-Ki;Jin, Yong-Tak;Oh, Sung-Kwun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.2
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    • pp.173-178
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    • 2014
  • In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.