• Title/Summary/Keyword: Principal Component Analysis

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A Study on the Vulnerability Assessment for Agricultural Infrastructure using Principal Component Analysis (주성분 분석을 이용한 농업생산기반의 재해 취약성 평가에 관한 연구)

  • Kim, Sung Jae;Kim, Sung Min;Kim, Sang Min
    • Journal of The Korean Society of Agricultural Engineers
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    • v.55 no.1
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    • pp.31-38
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    • 2013
  • The purpose of this study was to evaluate climate change vulnerability over the agricultural infrastructure in terms of flood and drought using principal component analysis. Vulnerability was assessed using vulnerability resilience index (VRI) which combines climate exposure, sensitivity, and adaptive capacity. Ten flood proxy variables and six drought proxy variables for the vulnerability assessment were selected by opinions of researchers and experts. The statistical data on 16 proxy variables for the local governments (Si, Do) were collected. To identify major variables and to explain the trend in whole data set, principal component analysis (PCA) was conducted. The result of PCA showed that the first 3 principal components explained approximately 83 % and 89 % of the total variance for the flood and drought, respectively. VRI assessment for the local governments based on the PCA results indicated that provinces where having the relatively large cultivation areas were categorized as vulnerable to climate change.

A Classification of Rural Area Using Principal Component Analysis and GIS (주성분 분석과 지리정보시스템을 이용한 충청북도 농촌 지역의 유형화)

  • Park, Jin-Sun;Joo, Ho-Gil;Yoon, Seong-Soo;Rhee, Shin-Ho
    • Proceedings of the Korean Society of Agricultural Engineers Conference
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    • pp.131-134
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    • 2003
  • The purpose of this study is for classification to do a short distance rural area with the object to the center to Cheongju area. This study used principal component analysis and geography information system, and it was disciplined oneself. It was done a study object region to Cheongju-si, Cheongwon-gun Goesan-gun, Eumseong-gun, and we divided an index by of 22 large class and 104 small class, and the SPSS analyzed the Principal Component Analysis. We used a Geography Information System, and it was made graphical data by the results that have finished Principal Component Analysis.

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A Comparative Study on Factor Recovery of Principal Component Analysis and Common Factor Analysis (주성분분석과 공통요인분석에 대한 비교연구: 요인구조 복원 관점에서)

  • Jung, Sunho;Seo, Sangyun
    • The Korean Journal of Applied Statistics
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    • v.26 no.6
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    • pp.933-942
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    • 2013
  • Common factor analysis and principal component analysis represent two technically distinctive approaches to exploratory factor analysis. Much of the psychometric literature recommends the use of common factor analysis instead of principal component analysis. Nonetheless, factor analysts use principal component analysis more frequently because they believe that principal component analysis could yield (relatively) less accurate estimates of factor loadings compared to common factor analysis but most often produce similar pattern of factor loadings, leading to essentially the same factor interpretations. A simulation study is conducted to evaluate the relative performance of these two approaches in terms of factor pattern recovery under different experimental conditions of sample size, overdetermination, and communality.The results show that principal component analysis performs better in factor recovery with small sample sizes (below 200). It was further shown that this tendency is more prominent when there are a small number of variables per factor. The present results are of practical use for factor analysts in the field of marketing and the social sciences.

Hierarchically penalized sparse principal component analysis (계층적 벌점함수를 이용한 주성분분석)

  • Kang, Jongkyeong;Park, Jaeshin;Bang, Sungwan
    • The Korean Journal of Applied Statistics
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    • v.30 no.1
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    • pp.135-145
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    • 2017
  • Principal component analysis (PCA) describes the variation of multivariate data in terms of a set of uncorrelated variables. Since each principal component is a linear combination of all variables and the loadings are typically non-zero, it is difficult to interpret the derived principal components. Sparse principal component analysis (SPCA) is a specialized technique using the elastic net penalty function to produce sparse loadings in principal component analysis. When data are structured by groups of variables, it is desirable to select variables in a grouped manner. In this paper, we propose a new PCA method to improve variable selection performance when variables are grouped, which not only selects important groups but also removes unimportant variables within identified groups. To incorporate group information into model fitting, we consider a hierarchical lasso penalty instead of the elastic net penalty in SPCA. Real data analyses demonstrate the performance and usefulness of the proposed method.

THE ANALYSIS AND DIAGNOSIS OF SOWN PASTURE VEGETATION 2. GROUPING AND CHARACTERIZATION THE SOWN AND WEED SPECIES BY MEANS OF PRINCIPAL COMPONENT ANALYSIS

  • Kawanabe, S.
    • Asian-Australasian Journal of Animal Sciences
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    • v.4 no.3
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    • pp.245-250
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    • 1991
  • Analysis of the characteristics and the grouping of the species of sown and weeds in artificial pastures was studied applying the principal component analysis method. Presency and coverage of six sown species and fifteen weed species which occurred in pastures of under-grazing and optimumgrazing were subject to analysis. From field survey, species were divided into three groups: the group A included five species such as Festuca arundinacea, Lolium perenne and Dactylis glomerata, etc., the group B included eleven species such as Polygonum longisetum, Agrostis alba and Rumex obtusifolius, etc., and the group C included five species such as Miscanthus sinensis, Rubus palmatus and Artemisia princeps, etc. The group A species corresponded to good pasture conditions and management. On the contrary, the group C species occurred in poor pasture conditions with inadequate management. The group B species corresponded to intermediate pasture conditions and management. Interrelated pair species co-existing and species non-co-existing were discovered. Factor loading as negative for the group A species. positive for the group C species and positive but lower than the group C species for the group B species. From these results it is concluded that the principal component analysis seems to one of the useful tools for the analysis of characteristics of species and the diagnosis of sown pasture vegetation, although further studies are required to get more general information about species characteristics.

Principal Component Analysis of Compositional Data using Box-Cox Contrast Transformation (Box-Cox 대비변환을 이용한 구성비율자료의 주성분분석)

  • 최병진;김기영
    • The Korean Journal of Applied Statistics
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    • v.14 no.1
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    • pp.137-148
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    • 2001
  • Compositional data found in many practical applications consist of non-negative vectors of proportions with the constraint which the sum of the elements of each vector is unity. It is well-known that the statistical analysis of compositional data suffers from the unit-sum constraint. Moreover, the non-linear pattern frequently displayed by the data does not facilitate the application of the linear multivariate techniques such as principal component analysis. In this paper we develop new type of principal component analysis for compositional data using Box-Cox contrast transformation. Numerical illustrations are provided for comparative purpose.

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Pattern Classification of PM -10 in the Indoor Environment Using Disjoint Principal Component Analysis (분산주성분 분석을 이용한 실내환경 중 PM-10 오염의 패턴분류)

  • 남보현;황인조;김동술
    • Journal of Korean Society for Atmospheric Environment
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    • v.18 no.1
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    • pp.25-37
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    • 2002
  • The purpose of the study was to survey the distribution patterns of inorganic elements of PM-10 in the various indoor environments and analyze the pollution patterns of aerosol in various places of indoor environment using a pattern recognition method based on cluster analysis and disjoint principal component analysis. A total of 40 samples in the indoor had been collected using mini-vol portable samplers. These samples were analyzed for their 19 bulk inorganic compounds such as B, Na, Mg, Al, K, Ca, Ti, V, Cr, Fe, Ni, Cu, Zn, As, Se, Cd, Ba, Ce, and Pb by using an ICP-MS. By applying a disjoint principal component analysis, four patterns of the indoor air pollutions were distinguished. The first pattern was identified as a group with high concentrations of PM-10, Na, Mg, and Ca. The second pattern was identified as a group with high concentrations B, Mg, At, Ca, Fe, Cu, and Ba. The third pattern was a group of sites with high concentrations of K, Zn. Cd. The fourth pattern was a group with low concentrations PM-10 and all inorganic elements. This methodology was found to be helpful enough to set the criteria standard of indoor air quality, corresponding pollutants, and classification of indoor environment categories when making an indoor air quality law.