• Title, Summary, Keyword: Principal component analysis

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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.

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.

Assessment and Classification of Korean Indigenous Corn Lines by Application of Principal Component Analysis (주성분분석에 의한 재래종 옥수수의 해석)

  • 이인섭;박종옥
    • Journal of Life Science
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    • v.13 no.3
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    • pp.343-348
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    • 2003
  • This study was conducted to get basic information on the Korean local corn line collected from Busan City and Kyungnam Province, a total of 49 lines were selected and assessed by the principal component analysis method. In the result of principal component analysis for 7 characteristics, 67.4% and 86.3% of total variation could be appreciated by the first two and first four principal components, respectively. Contribution of characteristics to principal component was high at upper principal components and low at lower principal components. Biological meaning of principal component and plant types corresponding to the each principal component were explained clearly by the correlation coefficient between principal component and characteristics. The first principal component appeared to correspond to the size of plant and ear, and the duration of vegetative growing period. The second principal component appeared to correspond to the number of ear and tiller. But the meaning of the third and fourth principal components were not clear.

Partial Quantification in Principal Component Analysis

  • Hye Sun Suh;Myung Hoe Huh
    • Communications for Statistical Applications and Methods
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    • v.4 no.3
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    • pp.637-644
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    • 1997
  • Sometimes, the first principal component may come logically from the established knowledge and premises. For example, for the high school students' test scores of Korean, English, Mathematics, Social Study, and Science, it is natural to define the first principal component as the average of all subject scores. In such cases, we need to respect both the background knowledge and the data exploration. The aim of this study is to find the remaining components in principal component analysis of multivariate data when the first principal component is defined a priori by the researcher. Moreover, we study related matrix decomposition and their application to the graphical display.

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Sensitivity Analysis in Principal Component Regression with Quadratic Approximation

  • Shin, Jae-Kyoung;Chang, Duk-Joon
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.3
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    • pp.623-630
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    • 2003
  • Recently, Tanaka(1988) derived two influence functions related to an eigenvalue problem $(A-\lambda_sI)\upsilon_s=0$ of real symmetric matrix A and used them for sensitivity analysis in principal component analysis. In this paper, we deal with the perturbation expansions up to quadratic terms of the same functions and discuss the application to sensitivity analysis in principal component regression analysis(PCRA). Numerical example is given to show how the approximation improves with the quadratic term.

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Simple principal component analysis using Lasso (라소를 이용한 간편한 주성분분석)

  • Park, Cheolyong
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.3
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    • pp.533-541
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    • 2013
  • In this study, a simple principal component analysis using Lasso is proposed. This method consists of two steps. The first step is to compute principal components by the principal component analysis. The second step is to regress each principal component on the original data matrix by Lasso regression method. Each of new principal components is computed as the linear combination of original data matrix using the scaled estimated Lasso regression coefficient as the coefficients of the combination. This method leads to easily interpretable principal components with more 0 coefficients by the properties of Lasso regression models. This is because the estimator of the regression of each principal component on the original data matrix is the corresponding eigenvector. This method is applied to real and simulated data sets with the help of an R package for Lasso regression and its usefulness is demonstrated.

Principal component regression for spatial data (공간자료 주성분분석)

  • Lim, Yaeji
    • The Korean Journal of Applied Statistics
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    • v.30 no.3
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    • pp.311-321
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    • 2017
  • Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.

Catch Specification of Japanese Tuna Purse Seine in the Western Pacific Ocean (서부태평야지역에서 일본 다랑어선망어업의 어획특성)

  • 김형석
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.35 no.3
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    • pp.243-249
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    • 1999
  • Specificity of catches has been analyzed to japanese tuna purse seine A principle component analysis was used to improve the efficiency of fishing and increase sustainable production and productivity of Korean tuna purse seine.The result are as follows;From the principal component analysis of the fish catches, the first principal component(Z1) to promote principal component score was skipjack Kastsuwonus Pelamis, LINNAEUS and yellowfin tuna Thunnus Albacares, BONNATERRE (Small : smaller than 10kg) and proportion was 86.8% of total. The second principal component(Z2) to increase principal component score was yellowfin tuna (Large : larger than 10kg) and proportion was 9.5%.On the other hand, fish operating that have caught skipjack and yellowfin tuna (Small and Larger) was not so much. Fish catches for one species raised volume of the catches while catches for multi-species decreased it since principal composition score for one species and both species together has been increased.Fish school could be divided into three groups of schools each of which was associated with drift objects, payaho and ship, school associated with shark, whale and porpoise and school of breezing, feeding and jumping from proportion of principal component analysis for fish catches of school types. However, the biological pattern is different among school associated with ship, payaho and school associated with drift objects for analysis eigen vector. School associated with ship, payaho and school associated with drifting object associated is judged as school which be assembled to vessel and drifted log temporary.

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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.