• Title/Summary/Keyword: Principal Component Analysis

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Asymptotic Test for Dimensionality in Probabilistic Principal Component Analysis with Missing Values

  • Park, Chong-sun
    • Communications for Statistical Applications and Methods
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    • v.11 no.1
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    • pp.49-58
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    • 2004
  • In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

Risk Evaluation of Slope Using Principal Component Analysis (PCA) (주성분분석을 이용한 사면의 위험성 평가)

  • Jung, Soo-Jung;Kim, -Yong-Soo;Kim, Tae-Hyung
    • Journal of the Korean Geotechnical Society
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    • v.26 no.10
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    • pp.69-79
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    • 2010
  • To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.

Arrow Diagrams for Kernel Principal Component Analysis

  • Huh, Myung-Hoe
    • Communications for Statistical Applications and Methods
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    • v.20 no.3
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    • pp.175-184
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    • 2013
  • Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.

Application of the Principal Component Analysis to Evaluate Concrete Condition Using Impact Resonance Test (충격공진을 이용한 콘크리트 상태 평가를 위한 주성분 분석의 적용)

  • Yoon, Young Geun;Oh, Tae Keun
    • Journal of the Korean Society of Safety
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    • v.34 no.5
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    • pp.95-102
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    • 2019
  • Non-destructive methods such as rebound hardness method and ultrasonic method are widely studied for evaluating the physical properties, condition and damage of concrete, but are not suitable for detecting delamination and cracks near the surface due to various constraints of the site as well as the accuracy. Therefore, in this study, the impact resonance method was applied to detect the separation cracks occurring near the surface of the concrete slab and bridge deck. As a next step, the principal component analysis were performed by extracting various features using the FFT data. As a result of principal component analysis, it was analyzed that the reliability was high in distinguishing defects in concrete. This feature extraction and application of principal component analysis can be used as basic data for future use of machine learning technique for the better accuracy.

Classification of honeydew and blossom honeys by principal component analysis of physicochemical parameters

  • Choi, Suk-Ho;Nam, Myoung Soo
    • Korean Journal of Agricultural Science
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    • v.47 no.1
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    • pp.67-81
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    • 2020
  • The physicochemical parameters of honey are used to determine the botanic origin of honey and to specify the composition criteria for honey in regulations and standards. The parameters of honeydew and blossom honeys from Korean beekeepers were determined to investigate whether they complied with the composition criteria for honey in the food code legislated by Korean authority and to establish the parameters which should be subjected to principal component analysis for improved differentiation of honeys. The fructose and glucose contents of the honeydew honey did not comply with the composition criteria. The ash content of the honey was closely correlated with CIE a* and CIE L* The principal component analysis of fructose to glucose ratio, CIE a*, CIE L*, ash content, free acidity, and fructose and glucose contents enabled classification of honeydew, chestnut, multifloral, and acacia honeys. Additional advantage of the principal component analysis (PCA) is that the physicochemical parameters, such as fructose to glucose ratio (F/G) and color, can be determined using the analytical instruments for composition criteria and quality control of honey. This study suggested that composition criteria for honeydew honey should be established in the food code in accordance with international standards. The principal component analysis reported in this study resulted in improved classification of the honeys from Korean beekeepers.

Application of the supplementary principal component analysis for the 1982-1992 Korean Pro Baseball data (89-92 한국 프로야구의 각 팀과 부문별 평균 성적에 대한 추가적 주성분분석의 응용)

  • 최용석;심희정
    • The Korean Journal of Applied Statistics
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    • v.8 no.1
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    • pp.51-60
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    • 1995
  • Given an $n \times p$ data matrix, if we add the $p_s$ variables somewhat different nature than the p variables to this matrix, we have a new $n \times (p+p_s)$ data matrix. Because of these $p_s$ variables, the traditional principal component analysis can't provide its efficient results. In this study, to improve this problem we review the supplementary principal component analysis putting $p_s$ variables to supplementary variable. This technique is based on the algebraic and geometric aspects of the traditional principal component analysis. So we provide a type of statistical data analysis for the records of eight teams and fourteen fields of the 1982-1992 Korean Pro Baseball Data based on the supplementary principal component analysis and the traditional principal component analysis. And we compare the their results.

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Speaker Identification on Various Environments Using an Ensemble of Kernel Principal Component Analysis (커널 주성분 분석의 앙상블을 이용한 다양한 환경에서의 화자 식별)

  • Yang, Il-Ho;Kim, Min-Seok;So, Byung-Min;Kim, Myung-Jae;Yu, Ha-Jin
    • The Journal of the Acoustical Society of Korea
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    • v.31 no.3
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    • pp.188-196
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    • 2012
  • In this paper, we propose a new approach to speaker identification technique which uses an ensemble of multiple classifiers (speaker identifiers). KPCA (kernel principal component analysis) enhances features for each classifier. To reduce the processing time and memory requirements, we select limited number of samples randomly which are used as estimation set for each KPCA basis. The experimental result shows that the proposed approach gives a higher identification accuracy than GKPCA (greedy kernel principal component analysis).

Evaluation of Panel Performance by Analysis of Variance, Correlation Analysis and Principal Component Analysis (패널요원 수행능력 평가에 사용된 분산분석, 상관분석, 주성분분석 결과의 비교)

  • Kim, Sang-Sook;Hong, Sung-Hie;Min, Bong-Kee;Shin, Myung-Gon
    • Korean Journal of Food Science and Technology
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    • v.26 no.1
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    • pp.57-61
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    • 1994
  • Performance of panelists trained for cooked rice quality was evaluated using analysis of variance, correlation analysis, and principal component analysis. Each method offered different information. Results showed that panleists with high F ratios (p=0.05) did not always have high correlation coefficient (p=0.05) with mean values pooled from whole panel. The results of analysis of variance for the panelists whose performance were extremely good or extremely poor were consistent with those of correlation analysis. Outliers designated by principal component analysis were different from the panelists whose performance was defined as extremely good or extremely poor by analysis of variance and correlation analysis. The results of principal component analysis descriminated the panelists with different scoring range more than different scoring trends depending on the treatments. Our study suggested combination of analysis of variance and correlation analysis provided valid basis for screening panelists.

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Improvement on Fuzzy C-Means Using Principal Component Analysis

  • Choi, Hang-Suk;Cha, Kyung-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.301-309
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    • 2006
  • In this paper, we show the improved fuzzy c-means clustering method. To improve, we use the double clustering as principal component analysis from objects which is located on common region of more than two clusters. In addition we use the degree of membership (probability) of fuzzy c-means which is the advantage. From simulation result, we find some improvement of accuracy in data of the probability 0.7 exterior and interior of overlapped area.

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Application of Principal Component Analysis Prior to Cluster Analysis in the Concept of Informative Variables

  • Chae, Seong-San
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.1057-1068
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    • 2003
  • Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.