• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.945-956
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• 2021

In this study, we considered the algorithm of Legendre and Legendre (2012), which derives canonical correspondence analysis from weighted principal component analysis. And, it was proved that the canonical correspondence analysis based on the weighted principal component analysis is exactly the same as Ter Braak's (1986) canonical correspondence analysis based on the Gaussian response model. Ter Braak (1986)'s canonical correspondence analysis derived from a Gaussian response curve that can explain the abundance of species in ecology well uses the basic assumption of the species packing model and then conducts generalized linear model and canonical correlation analysis. It is derived by way of binding. However, the algorithm of Legendre and Legendre (2012) is calculated in a method quite similar to Benzecri's correspondence analysis without such assumptions. Therefore, if canonical correspondence analysis based on weighted principal component analysis is used, it is possible to have some flexibility in using the results. In conclusion, this study shows that the two methods starting from different models have the same site scores, species scores, and species-environment correlations.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.367-379
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• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Journal of Power Electronics
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• v.16 no.1
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• pp.182-189
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• 2016

Fault detection is the research focus and priority in this study to ensure the high reliability of a proposed three-level inverter. Kernel principal component analysis (KPCA) has been widely used for feature extraction because of its simplicity. However, highlighting useful information that may be hidden under retained KPCs remains a problem. A weighted KPCA is proposed to overcome this shortcoming. Variable contribution plots are constructed to evaluate the importance of each KPC on the basis of sensitivity analysis theory. Then, different weighting values of KPCs are set to highlight the useful information. The weighted statistics are evaluated comprehensively by using the improved feature eigenvectors. The effectiveness of the proposed method is validated. The diagnosis results of the inverter indicate that the proposed method is superior to conventional KPCA.

• Journal of Korea Water Resources Association
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• v.48 no.9
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• pp.757-768
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• 2015

Recently, climate change has affected short time concentrated local rainfall and unexpected heavy rain which is increasingly causing life and property damage. In this research, arithmetic average analysis, weighted average analysis, and principal component analysis are used for predicting flood risk. This research is foundation for application of predicting flood risk based on annals of disaster and status of urban planning. Results obtained by arithmetic average analysis, weighted average analysis, and principal component analysis using many factors affect on flood are compared. In case of arithmetic average analysis, each factor has same weights though it is simple method. In case of weighted average analysis, correlation factors are complex by many variables and multicollinearty problem happen though it has different weights. For solving these problems, principal component analysis (PCA) is used because each factor has different weights and the number of variables is smaller than other methods by combining variables. Finally, flood risk assessment considering flood control ability and urban environment risk in former research is predicted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1240-1250
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• 2010

In this paper, an eye tracking method is presented by using on iterated spatial moment adapting weighted gray level that can accurately detect and track user's eyes under the complicated background. The region of face is detected by using Haar-like feature before extracting region of eyes to minimize an region of interest from the input picture of CCD camera. And the region of eyes is detected by using eigeneye based on the eigenface of Principal component analysis. Also, feature points of eyes are detected from darkest part in the region of eyes. The tracking of eyes is achieved correctly by using iterated spatial moment adapting weighted gray level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.198-201
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• 2009

In this paper, an eye tracking method is presented by using on iterated spatial moment adapting weighted gray level that can accurately detect and track user's eyes under the complicated background. The region of face is detected by using Haar-like feature before extracting region of eyes to minimize an region of interest from the input picture of CCD camera. And the region of eyes is detected by using eigeneye based on the eigenface of Principal component analysis. And then feature points of eyes are detected from darkest part in the region of eyes. The tracking of eyes is achieved correctly by using iterated spatial moment adapting weighted gray level.

• International Journal of Contents
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• v.1 no.2
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• pp.1-4
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• 2005

In this paper, we compare 5 weighted distance measures between feature vectors with respect to the recognition performance of the principal component analysis(PCA)-based face recognition method, and propose modified weighted distance. The proposed method was modification of z, the weighted vector. The simulation was performed using the ORL face database, showed the best result for some weighted distances such as weighted manhattan, weighted angle-based, weighted modified manhattan, and weighted modified SSE. We also showed that using some various values of z(weighted values) we can achieve better recognition results that using the existing weighted value.

• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.17-28
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• 1999

최근 한의학 분야에서도 객관성 확보를 위한 학문적 경험의 수량화연구가 일반화되고 있다. 이의 일환으로 한방부인과의 진단 기준 프로그램개발이 요구되고 있으며 본 논문에서는 이에 대한 해결책으로 가중주성분분석을 이용한 변증점수 개발을 제안하고 있다. 또한 이 과정에서 변증유형별 점수 비교를 하기 위한 변증점수의 사후조정에 대한 방법도 제시하고있다.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1271-1281
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• 2016

Wins above replacement (WAR) is one of the most widely used statistic among sabermatrics statistics that measure the ability of a batter in baseball. WAR has a great advantage that is to represent the attack power of the player and the base running ability, defensive ability as a single value. In this study, we proposed a hitter ability index using the sabermetrics statistics that can replace WAR based on Korea Baseball Record Data of the last three years (2013-2015). First, we calculated Batter ability index through the arithmetic mean method, the weighted average method, principal component regression and selected the method that had high correlation with WAR.

• Journal of Korean Society of Forest Science
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• v.78 no.3
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• pp.255-262
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• 1989

Vegetation data of the mixed mesophytic forest in Mt. Chombong area were analyzed by the methods of classification and ordination. 'Weighted group average linkage cluster analysis' recognized five distinctive vegetation groups, based on the abundance data of 83 woody plant species in 70 sampling units. The species diversity was also examined for each group. The importance values of 42 tree species in the groups were subjected to principal component analysis (PCA). The PCA ordinated five vegetation groups on the first two axes, so as to compare similarity among them in terms of species composition. Acer palmatum, Fraxinus rhynchophylla, Quercus mongolica, and Acer mono had greatest influence on the determination of group scores with high eigenvectors (component loadings) in the first axis. Distribution of these four dominant species appeared to be important in determining community association in this diversified forest.