• 제목/요약/키워드: 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.

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

  • 정수정;김용수;김태형
    • 한국지반공학회논문집
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    • v.26 no.10
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    • pp.69-79
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    • 2010
  • 본 연구에서는 사면의 이상 거동 및 붕괴 감지를 위해 실제 계측시스템 설치 후 이상보고가 있었던 사변을 대상으로 비모수적 통계방법인 주성분분석 (PCA : Principal Component Analysis)을 적용하였다. 분석결과, 사면의 이상거동여부를 나타내는 척도인 주성분점수는 이상징후 발생시 정상상태에 비해 상대적으로 크거나 낮은 값을 나타내어 변화량에 큰 차이를 보였다. 이를 통해 주성분 분석을 이용하여 사면의 이상 거동 및 붕괴를 감지할 수 있는 것을 확인하였다. 주성분분석을 활용하여 정량적인 사면거동 및 이상징후의 예측이 가능할 것으로 판단된다.

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.

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

  • 윤영근;오태근
    • 한국안전학회지
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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
    • 농업과학연구
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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.

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

  • 최용석;심희정
    • 응용통계연구
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    • v.8 no.1
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    • pp.51-60
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    • 1995
  • 크기가 $n \times p$인 자료행렬에서 p개의 변수들과 성격이 다소 다른 $p_s$개의 변수를 같이 고려한 크기가 $n \times (p + p_s)$ 자료행렬이 있다 하자. 전통적 주성성분분석은 성격이 다른 변수들로 인하여 효과적인 결과를 제공하지 못한다. 본 논문에서는 이런 점을 개선하기 위해서 성격이 다른 $p_s$개의 변수를 추가변수로 두는 추가적 주성분분석을 소개하려 한다. 이 기법은 전통적 주성분분석의 대수적,기하적인 면을 따른다. 그리고 전통적 주성분분석과 추가적 주성성분분석을 활용한 한국 프로야구의 8개팀과 1982-1992년 동안의 14개의 부문별 기록에 대한 전형적인 자료분석의 한 예를 제시한다. 더불어 두 분석의 결과도 비교하였다.

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

  • 양일호;김민석;소병민;김명재;유하진
    • 한국음향학회지
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    • v.31 no.3
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    • pp.188-196
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    • 2012
  • 본 논문에서는 커널 주성분 분석 (KPCA, kernel principal component analysis)으로 강화한 화자 특징을 이용하여 복수의 분류기를 학습하고 이를 앙상블 결합하는 화자 식별 방법을 제안한다. 이 때, 계산량과 메모리 요구량을 줄이기 위해 전체 화자 특징 벡터 중 일부를 랜덤 선택하여 커널 주성분 분석의 기저를 추정한다. 실험 결과, 제안한 방법이 그리디 커널 주성분 분석 (GKPCA, greedy kernel principal component analysis)보다 높은 화자 식별률을 보였다.

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

  • 김상숙;홍성희;민봉기;신명곤
    • 한국식품과학회지
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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.