• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2002년도 봄 학술발표논문집 Vol.29 No.1 (A)
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• pp.247-249
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• 2002

본 논문에서는 주성분 분석(principal component analysis; 이하 PCA) 및 독립성분분석(independent component analysis; 이하 ICA)을 이용, 이차원 영상을 분류하여 다중해상도에서 영상간의 거리를 측정하여 PCA 와 ICA 중에서 어느 것이 영상간의 상대적 식별을 용이하게 하는지 모의 실험을 통하여 확인하고자 한다. 모의 실험 결과로부터, ICA가 PCA에 비하여 영상간의 상대적 식별이 용이하여 빨리 수렴이 되는 것을 모의 실험을 통하여 확인하였다.

• 동아시아식생활학회지
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• 제16권1호
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• pp.23-28
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• 2006

The purpose of this paper was to clarify the actual conditions of the 'Dietary externalization' mainly by using the economic and nutrition-related data, accompanied by the economic development in Korea and Japan. 'Modernization of food style' and other modernization have taken place, among which 'Dietary externalization' in particular has recently drawn interest. At the time this paper clarified with econometric analysis whether there are differences between the two countries in term of the modernization of food style and dietary externalization trend. The trends of Dietary externalization of both Korea and Japan were studied using Principal Component Analysis method. The food subgroup were investigated based on the annual report on the household income and expenditure survey of Korea and the annual report on the family income and expenditure survey of Japan. The statistical data from both country were analyzed by SAS program. The results are as follows; 1. In Korea, the ratio of carbohydrates in the total calorie intake is quite high and animal protein is rather low compared to those in Japan. 2. Traditional food such as grains and vegetables are consumed much more in Korea than in Japan. 3. The Principal Component 1, 2 were extracted in both countries during the whole analysis period, which suggested the 'Dietary externalization' 4. Principal Component 1 has a positive factor loaded in all food items including meals outside the home and process food. In other words, it is apparent that the 'Dietary externalization' tread in Korea has a simple pattern suggesting that all externalization related items are on the rise. 5. Principal component 1, 2 which indicated the dietary externalization, were detected in Japan.

• 한국작물학회지
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• 제50권1호
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• pp.60-66
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• 2005

Pod shape of twenty soybean (Glycine max L. Merrill) genotypes was evaluated quantitatively by image analysis using elliptic Fourier descriptors and their principal components. The closed contour of each pod projection was extracted, and 80 elliptic Fourier coefficients were calculated for each contour. The Fourier coefficients were standardized so that they were invariant of size, rotation, shift, and chain code starting point. Then, the principal components on the standardized Fourier coefficients were evaluated. The cumulative contribution at the fifth principal component was higher than $95\%$, indicating that the first, second, third, fourth, and fifth principal components represented the aspect ratio of the pod, the location of the pod centroid, the sharpness of the two pod tips and the roundness of the base in the pod contour, respectively. Analysis of variance revealed significant genotypic differences in these principal components and seed number per pod. As the principal components for pod shape varied continuously, pod shape might be controlled by polygenes. It was concluded that principal component scores based on elliptic Fourier descriptors yield seemed to be useful in quantitative parameters not only for evaluating soybean pod shape in a soybean breeding program but also for describing pod shape for evaluating soybean germplasm.

• 응용통계연구
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• 제34권6호
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• pp.945-956
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• 2021

본 연구에서는 가중주성분분석으로부터 정준대응분석을 유도하는 Legendre와 Legendre (2012)의 알고리즘을 고찰하였다. 그리고, 가중주성분분석에 기반한 Legendre와 Legendre (2012)의 정준대응분석이 가우시안 반응모형에 기초한 Ter Braak (1986)의 정준대응분석과 동일함을 다루었다. 생태학에서 종의 발현 정도를 잘 설명할 수 있는 가우시안 반응곡선에서 도출된 Ter Braak (1986)의 정준대응분석은 종 패킹 모형(species packing model)이라는 기본 가정을 사용한 후 일반화선형모형과 정준상관분석을 결합시키는 방법으로 도출된다. 그런데 Legendre와 Legendre (2012)의 알고리즘은 이러한 가정없이 Benzecri의 대응분석과 상당히 유사한 방법으로 계산되는 특징을 지닌다. 그러므로 가중주성분석에 기초한 정준대응분석을 사용하면, 결과물 활용에 약간의 유연성을 지닐 수 있게 된다. 결론적으로 본 연구에서는 서로 다른 모형에서 출발한 두 방법이 장소점수(site score), 종 점수(species score) 그리고 환경변수와의 상관관계가 서로 동일함을 보인다.

• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.589-595
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• 2010

In discriminant analysis, we consider an intraclass correlation pattern by principal component analysis. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider two procedures, i.e., the test and proportion procedures, for selecting the principal components in classifica-tion. We compare the regular classification method and the proposed two procedures. We consider two methods for estimating error rate, i.e., the leave-one-out method and the bootstrap method.

• 대한원격탐사학회지
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• 제4권1호
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• pp.49-60
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• 1988

Principal Component Analysis (PCA) was applied for regional geological mapping to a multivariate data set of the Landsat TM data in the heavily vegetated and topographically rugged Chungju area. The multivariate data set selection was made by statistical analysis based on the magnitude of regression of squares in multiple regression, and it includes R1/2/R3/4, R2/3, R5/7/R4/3, R1/2, R3/4. R4/3. AND R4/5. As a result of application of PCA, some of later principal components (in this study PC 3 and PC 5) are geologically more significant than earlier major components, PC 1 and PC 2 herein. The earlier two major components which comprise 96% of the total information of the data set, mainly represent reflectance of vegetation and topographic effects, while though the rest represent 3% of the total information which statistically indicates the information unstable, geological significance of PC3 and PC5 in the study implies that application of the technique in more favorable areas should lead to much better results.

• Asian-Australasian Journal of Animal Sciences
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• 제4권3호
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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.

• Journal of the Korean Statistical Society
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• 제25권1호
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• pp.67-80
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• 1996

Factor analysis is a multivariate technique for describing the in-terrelationship among many variables in terms of a few underlying but unobservable random variables called factors. There are various approaches for this factor analysis. In particular, principal factor analysis is one of the most popular methods. This follows the mathematical algorithm of the principal component analysis based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, using the resistant singular value decomposition of Choi and Huh (1994), we derive a resistant principal factor analysis relatively little influenced by notable observations.

• Journal of the Korean Data and Information Science Society
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• 제7권1호
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• pp.113-118
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• 1996

주성분 분석은 자료압축, 특징추출, 통신이론, 패턴인식 그리고 화상처리등의 컴퓨터 공학분야에서 중요하게 사용되고 있다. Oja(1982,1989,1992)는 확률적 경사 강하법(SGA:Stochastic Gradient Ascent)을 이용한 제한된 헵규칙을 제안하여 주성분 분석에 사용하였다. 그러나, 이 규칙은 이상치에 민감하므로 이상치의 영향을 줄이기 위해, Xu & Yuille(1995)는 통계물리 방법을 이용한 로버스트 에너지함수를 생성하여 로버스트 주성분 분석방법을 제안하였다. 또한 Devlin et.al(1981)은 M-추정량을 이용하여 주성분 분석을 하였다. 본 논문에서는 Oja(1992)의 규칙과 Xu & Yuille(1995)의 로버스트 에너지함수를 이용하여 신경망을 구성하였다. 그리고, Devlin et.al(1981)이 제안한 시뮬레이션조건하에서 실험을 하였다. 실험한 결과와 Devlin et.al(1981)의 결과를 비교, 분석함으로써, 신경망의 성능을 확인하고자 한다.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 International Symposium on Clean Environment
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.