• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.7 s.144
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• pp.959-967
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• 2005

The color of apparels has the interaction of the face skin colors of the wearers. This study was carried out to classify the face skin colors of Korean males into several similar face skin colors in order to extract favorable colors which flatter to their face skin colors. The criterion that select the new subjects who have the classified face skin colors have to be decided. With color spectrometer, JX-777, face skin colors of subjects were measured quantitatively and classified into three clusters that had similar hue, value and chroma with Munsell Color System. Sample size was 418 Korean males and other 15 of new males subjects. Data were analyzed by K-means cluster analysis, ANOVA, Duncan multiple range test, Stepwise discriminant analysis using SPSS Win. 12. Findings were as follows: 1. 418 subjects who have YR colors were clustered into 3 kinds of face skin color groups. 2. Discriminant variables of face skin colors was 4 variables : L value of forehead, v value of cheek, c value of forehead, and b value of cheek from standardized canonical discriminant function coefficient 1 and c value of forehead, L value of forehead, b value of cheek. and L value of cheek from standardized canonical discriminant function coefficient 2. 3. Hit ratio of type 1 was $92.3\%$, of type 2 was $96.5\%$ and of type 3 was $92.6\%$ by the canonical discriminant function of 4 variables. 4. The canonical discriminant function equation 1 and 2 were calculated with the unstandardized canonical discriminant function coefficient and constant, the cutting score, and range of the score were computed. 5. The criterion that select the new subjects who have the classified face skin colors was decided.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.7 s.144
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• pp.978-986
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• 2005

The color of apparel products have a close relationship with the face skin colors of consumers. In order to extract the favorable colors which flatter to consumer's face skin colors, this study was carried our to classify the face skin colors of Korean females. The criteria that select new subjects who have the classified face skin colors have to be decided. With color spectrometer, JX-777, face skin colors of subjects were measured and classified into three clusters that had similar hue, value and chroma with Munsell Color System. Sample size was 324 Korean females and other new 10 college girls. Data were analyzed by K-means cluster analysis, ANOVA, Duncan multiple range test, Stepwise discriminant analysis using SPSS Win. 12. Findings were as follows: 1. 324 subjects who have YR colors were clustered into 3 face skin color groups. 2. Discriminant variables of face skin colors were 5 variables : b value of cheek, V value of forehead, L value of cheek, C value of forehead and H value of cheek by the standardized canonical discriminant function coefficient 1. 3. Hit ratio of type 1 was $96.8\%$, of type 2 was $94.9\%$, of type 3 was $100.0\%$ and mean of hit ratio was $96.9\%$ by canonical discriminant function of 5 variables. 4. With the unstandardized canonical discriminant function coefficient and constant, canonical discriminant function equation 1 and 2 were calculated. And cutting score and range of score of the classified types were computed. The criteria that select the new subjects were decided.

• Food Science of Animal Resources
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• v.29 no.1
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• pp.132-139
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• 2009

The objective of this study was to investigate the most effective analysis methods for palatability grading of Hanwoo beef by comparing the results of discriminant analysis with sensory data. The sensory data were obtained from sensory testing by 1,300 consumers evaluated tenderness, juiciness, flavor-likeness and overall acceptability of Hanwoo beef samples prepared by boiling, roasting and grilling cooking methods. For the discriminant analysis with one factor, overall acceptability, the linear discriminant functions and the non-parametric discriminant function with the Gaussian kernel were estimated. The linear discriminant functions were simple and easy to understand while the non-parametric discriminant functions were not explicit and had the problem of selection of kernel function and bandwidth. With the three palatability factors such as tenderness, juiciness and flavor-likeness, the canonical discriminant analysis was used and the ability of classification was calculated with the accurate classification rate and the error rate. The canonical discriminant analysis did not need the specific distributional assumptions and only used the principal component and canonical correlation. Also, it contained the function of 3 factors (tenderness, juiciness and flavor-likeness) and accurate classification rate was similar with the other discriminant methods. Therefore, the canonical discriminant analysis was the most proper method to analyze the palatability grading of Hanwoo beef.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.83-93
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• 2017

A typical approach to visualizing k (${\geq}2$)-group multidimensional data is to use Fisher's canonical discriminant analysis (CDA). CDA finds the best low-dimensional subspace that accommodates k group centroids in the Mahalanobis space. This paper proposes an alternative visualization procedure functioning in the Euclidean space, which finds the primary dimension with maximum discrimination of k group centroids and the secondary dimension with maximum dispersion of all observational units. This hybrid procedure is especially useful when the number of groups k is two.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.4
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• pp.267-271
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• 2000

A total of 1,830 soybean collections were grown in the field and characterized for 10 morphological traits to determine the diversity and relationship within and among geographical regions. Phenotypic variation was found within all regions for most characters. The Shannon-Weaver diversity index ranged from 0.49 to 0.62 across regions, and 0.09 to 1.00 across characters. Canonical discriminant analysis and clustering of the canonical means delineated 3 regional clusters: (ⅰ) Kyunggi, Chungchong, Kangwon, Chulla, and Kyungsang; (ⅱ) Heilongjiang; and (ⅲ) Jilin, Manchuria, central China, south China, Others (China), Hokkaido, Honshu, and Others (Japan).

• Korean journal of food and cookery science
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• v.18 no.1
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• pp.109-118
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• 2002

The purpose of this study was to identify the behavioral, attitudinal, and demographic correlates of light, medium, and heavy users of eating out at family restaurants. Among 358 reponses from the subjects, 224 responses were utilized for the analysis, and 134 responses were reserved for validating the discriminant function. Descriptive statistics, reliability analysis, stepwise discriminant analysis, canonical discriminant analysis, and anova analysis were used for this study. The findings from this study were as follows: First, He behavioral characteristics were found to discriminate among the three usage groups. Second, it was found that heavy users expressed greater difference between perception and expectation on the quantity of food that are appropriately served and the consistent quality of food at every visit. Third, the usage rate of eating out was not dependent on the sex, but dependent on the companion, average expenditure, and the time of eating out in chi-square test. Finally, the results of the study provide some insight into the pattern of marketing strategies that can be successfully used by the managers of family restaurants.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.297-306
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• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Journal of Korean Society for Quality Management
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• v.8 no.1
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• pp.8-15
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• 1980

There are many methods of pattern recognition. In this paper we assume that the responses of independent m groups are described by p-variate normal random variables with distinct mean vectors and a common covariance matrix. Under the assumption we give pattern recognition of m groups by means of canonical discrininant analysis and it's computer program. An example is presented.

• Journal of Korean Academy of Nursing Administration
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• v.13 no.4
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• pp.462-472
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• 2007

Purpose: Purpose of the this study is to define the hemodialysis noncompliance Indicators and discriminant standards levels for low Flux Hemodialysis patients and development of Hemodialysis noncompliance measurement - brief form. Method: Data was collected from 269 hemodialysis patients. To establish the hemodialysis noncompliance Indicators and to discriminate standards, 13 hemodialysis nurses and 2 nephrology doctors are participated in professional group. To verify the indicators and discriminant standards, data was ananlyzed by the canonical discriminant analysis method using by SAS 8.3 program. Result: 4 Indicators- interdialysis weight gain(IWG); average of recent 4weeks, serum phophate level, skipping of hemodialysis and hemodialysis time shortening without permission- of hemodialysis noncompliance are established and discriminant standards are developed. Discriminant ability of these 4 noncompliance indicators is 99.7%(p=.000). Hemodialysis noncompliance measurement - brief form has 96.3% discriminant accuracy. Conclusion: Hemodialysis noncompliant patients have high risks. It means that special intervention to noncompliance is needed. Also continuous and objective assessment and standards of noncompliance are needed.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.517-525
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• 1999

본 논문에서는 정준판별분석에서 분석자의 사전지식을 고려한 조건부 계량화(partial quantification)를 제안하고 있다. 연관된 수식을 라그랑지 승수법으로 유도하였으며 SAS/IML을 이용한 프로그램과 예제를 간략히 설명하였다.