• Korean Journal of Food Science and Technology
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• v.26 no.1
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• pp.57-61
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• 1994

Performance of panelists trained for cooked rice quality was evaluated using analysis of variance, correlation analysis, and principal component analysis. Each method offered different information. Results showed that panleists with high F ratios (p=0.05) did not always have high correlation coefficient (p=0.05) with mean values pooled from whole panel. The results of analysis of variance for the panelists whose performance were extremely good or extremely poor were consistent with those of correlation analysis. Outliers designated by principal component analysis were different from the panelists whose performance was defined as extremely good or extremely poor by analysis of variance and correlation analysis. The results of principal component analysis descriminated the panelists with different scoring range more than different scoring trends depending on the treatments. Our study suggested combination of analysis of variance and correlation analysis provided valid basis for screening panelists.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.487-492
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• 2008

Facial expression recognition is an intensive research area for designing Human Computer Interfaces. In this work, we present a new facial expression recognition system utilizing Enhanced Independent Component Analysis (EICA) for feature extraction and discrete Hidden Markov Model (HMM) for recognition. Our proposed approach for the first time deals with sequential images of emotion-specific facial data analyzed with EICA and recognized with HMM. Performance of our proposed system has been compared to the conventional approaches where Principal and Independent Component Analysis are utilized for feature extraction. Our preliminary results show that our proposed algorithm produces improved recognition rates in comparison to previous works.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8101-8105
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• 2014

Background: Although mucinous adenocarcinoma has been recognized for a long time, whether it is associated with a poorer prognosis in colorectal cancer patients is still controversial. Many studies put emphasis on mucinous adenocarcinoma containing mucin component ${\geq}50%$. Only a few studies have analyzed cases with a mucin component <50%. Objectives: This study aimed to analyze the prognostic value of different mucin component proportions in patients with stage III rectal cancer. Materials and Methods: Clinical, pathological and follow-up data of 136 patients with the stage III rectal cancer were collected. Every variable was analyzed by univariate analysis, then multivariate analysis and survival analysis were further performed. Results: Univariate analysis showed pathologic T stage, lymphovascular invasion, and histological subtype were statistically significant for DFS. Pathologic T stage was significant for OS. Histological subtype and lymphovascular invasion were independent prognostic factors in multivariate analysis for DFS, and histological subtype was the only independent prognostic factor for OS. Survival curves showed the survival time of mucinous adenocarcinoma (MUC) was shorter than non-MUC (adenocarcinomas with a mucin component <50% and without mucin component). Conclusions: Histological subtype (tumor with different mucin component) was an independent prognostic factor for both DFS and OS. Patients with MUC had a worse prognosis than their non-MUC counterparts with stage III rectal carcinoma.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.985-994
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• 2005

We examined problems of the principal component analysis(PCA), which is able to analyze at the low dimensionality as a methodologv to assess hydrologic time series, and introduced the theory and characteristics of independent component analysis(ICA) that can supplement problems of principal component analysis. We also applied the global sea surface temperature(SST) of the Nino region and assessed the correlation between El $\tilde{n}ino$-Southern Oscillation(ENSO) and SST. The results of examining separation-ability of principal components using mixed signals indicate that the independent component analysis is statistically superior compared to that of the principal component analysis. Finally, we assessed correlation between ENSO and global anomaly SST. The independent component analysis was applied to the $5^{\circ}{\times}5^{\circ}$(latitude and longitude) global anomaly SST in the Nino+3.4 region that is the El $\tilde{n}ino$ observation section. We assessed the correlation with the ENSO years. These results of the analysis show that only one independent component($86\%$) was able to represent the entire behavior and was consistent with the main ENSO years. Finally, we carried out independent component analysis for summer seasonal rainfalls at nine stations and could extract ICs to reflect geographical characteristics. The increasing trend has been shown at IC-1 and IC-2 since 1970s.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.51-60
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• 1995

Given an $n \times p$ data matrix, if we add the $p_s$ variables somewhat different nature than the p variables to this matrix, we have a new $n \times (p+p_s)$ data matrix. Because of these $p_s$ variables, the traditional principal component analysis can't provide its efficient results. In this study, to improve this problem we review the supplementary principal component analysis putting $p_s$ variables to supplementary variable. This technique is based on the algebraic and geometric aspects of the traditional principal component analysis. So we provide a type of statistical data analysis for the records of eight teams and fourteen fields of the 1982-1992 Korean Pro Baseball Data based on the supplementary principal component analysis and the traditional principal component analysis. And we compare the their results.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.395-398
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• 2003

Two component analysis methods based on the fuzzy relational calculus are proposed in the setting of the ordered structure. First component analysis is based on a decomposition of fuzzy relation into fuzzy bases, using gradient method. Second one is a component analysis based on the eigen fuzzy sets of fuzzy relation. Through experiments using the test images extracted from SIDBA and View Sphere Database, the effectiveness of the proposed component analysis methods is confirmed. Furthermore, improvements of the image compression/reconstruction and image retrieval based on ordered structure are also indicated.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.401-406
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine(SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel(KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.