• Title/Summary/Keyword: Principal Component Analysis

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A Penalized Principal Component Analysis using Simulated Annealing

  • Park, Chongsun;Moon, Jong Hoon
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.1025-1036
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    • 2003
  • Variable selection algorithm for principal component analysis using penalty function is proposed. We use the fact that usual principal component problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Simulated annealing algorithm is used in searching for optimal solutions with penalty functions. Comparisons between several well-known penalty functions through simulation reveals that the HARD penalty function should be suggested as the best one in several aspects. Illustrations with real and simulated examples are provided.

Principal Component Analysis of BGP Update Streams

  • Xu, Kuai;Chandrashekar, Jaideep;Zhang, Zhi-Li
    • Journal of Communications and Networks
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    • v.12 no.2
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    • pp.191-197
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    • 2010
  • In this paper, we propose a novel methodology to identify border gateway protocol (BGP) updates associated with major events - affecting network reachability to multiple ASes - and separate them (statistically) from those attributable to minor events, which individually generate few updates, but collectively form the persistent background noise observed at BGP vantage points. Our methodology is based on principal component analysis, which enables us to transform and reduce the BGP updates into different AS clusters that are likely affected by distinct major events. We demonstrate the accuracy and effectiveness of our methodology through simulations and real BGP data.

Robust Design for Multiple Quality Characteristics using Principal Component Analysis

  • Kwon, Yong-Man;Hong, Yeon-Woong
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.3
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    • pp.545-551
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    • 2003
  • Robust design is to identify appropriate settings of control factors that make the system's performance robust to changes in the noise factors that represent the source of variation. In this paper we propose how to simultaneously optimize multiple quality characteristics using the principal component analysis of multivariate statistical analysis. An example is illustrated to compare it with already proposed method.

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Motion Recognition using Principal Component Analysis

  • Kwon, Yong-Man;Kim, Jong-Min
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.817-823
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    • 2004
  • This paper describes a three dimensional motion recognition algorithm and a system which adopts the algorithm for non-contact human-computer interaction. From sequence of stereos images, five feature regions are extracted with simple color segmentation algorithm and then those are used for three dimensional locus calculation precess. However, the result is not so stable, noisy, that we introduce principal component analysis method to get more robust motion recognition results. This method can overcome the weakness of conventional algorithms since it directly uses three dimensional information motion recognition.

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Incremental Eigenspace Model Applied To Kernel Principal Component Analysis

  • Kim, Byung-Joo
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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    • pp.345-354
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    • 2003
  • An incremental kernel principal component analysis(IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis(KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenvectors should be recomputed. IKPCA overcomes this problem by incrementally updating the eigenspace model. IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the classification problem on nonlinear data set.

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Resistant Singular Value Decomposition and Its Statistical Applications

  • Park, Yong-Seok;Huh, Myung-Hoe
    • Journal of the Korean Statistical Society
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    • v.25 no.1
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    • pp.49-66
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    • 1996
  • The singular value decomposition is one of the most useful methods in the area of matrix computation. It gives dimension reduction which is the centeral idea in many multivariate analyses. But this method is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, we derive the resistant version of singular value decomposition for principal component analysis. And we give its statistical applications to biplot which is similar to principal component analysis in aspects of the dimension reduction of an n x p data matrix. Therefore, we derive the resistant principal component analysis and biplot based on the resistant singular value decomposition. They provide graphical multivariate data analyses relatively little influenced by outlying observations.

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Classification of papers using IR and NIR spectra and principal component analysis (IR 및 NIR 스펙트럼과 주성분 분석을 통한 지종의 분류)

  • Kim, Kang-Jae;Eom, Tae-Jin
    • Journal of Korea Technical Association of The Pulp and Paper Industry
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    • v.48 no.1
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    • pp.34-42
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    • 2016
  • In this study, we classified three copying papers and Korean, Chinese, and Japanese traditional papers using IR and/or NIR spectra and principal component analysis. Various chemicals are used when producing fine papers. In this case, the IR method to analyze functional groups is suitable for the classification of paper. On the other hand, NIR analysis is more suitable for the classification of traditional papers, as it uses nearly raw materials (pulp). Therefore, principal component analysis using IR and NIR depending on the paper production process will be the classification tool of paper.

ECG based Personal Authentication using Principal Component Analysis (주성분 분석기법을 이용한 심전도 기반 개인인증)

  • Cho, Ju-Hee;Cho, Byeong-Jun;Lee, Dae-Jong;Chun, Myung-Geun
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.66 no.4
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    • pp.258-262
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    • 2017
  • The PCA(Principal Component Analysis) algorithm is widely used as a technique of expressing the eigenvectors of the covariance matrix that best represents the characteristics of the data and reducing the high dimensional vector to a low dimensional vector. In this paper, we have developed a personal authentication method based on ECG using principal component analysis. The proposed method showed excellent recognition performance of 98.2 [%] when it was experimented using electrocardiogram data obtained at weekly intervals. Therefore, it can be seen that it is useful for personal authentication by reducing the dimension without changing the information on the variability and the correlation set variable existing in the electrocardiogram data by using the principal component analysis technique.

Global Covariance based Principal Component Analysis for Speaker Identification (화자식별을 위한 전역 공분산에 기반한 주성분분석)

  • Seo, Chang-Woo;Lim, Young-Hwan
    • Phonetics and Speech Sciences
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    • v.1 no.1
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    • pp.69-73
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    • 2009
  • This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.

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Moving Window Principal Component Analysis for Detecting Positional Fluctuation of Spectral Changes

  • Ryu, Soo-Ryeon;Noda, Isao;Jung, Young-Mee
    • Bulletin of the Korean Chemical Society
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    • v.32 no.7
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    • pp.2332-2338
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    • 2011
  • In this study, we proposed a new promising idea of utilizing moving window principal component analysis (MWPCA) as a sensitive diagnostic tool to detect the presence of peak position shift. In this approach, the moving window is constructed from a small data segment along the wavenumber axis. For each window bound by a narrow wavenumber region, separate PCA analysis was applied. Simulated spectra with complex spectral feature variations were analyzed to explore the possibility of MWPCA technique. This MWPCA-based detection of the peak shift, potentially coupled with 2D correlation analysis to provide additional verification, may offer an attractive solution.