• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1338-1347
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• 2013

Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher's linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.

• Phonetics and Speech Sciences
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• v.9 no.4
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• pp.27-34
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• 2017

The purpose of this work is to examine the English and Korean voiceless plosives from the Buckeye[15] and Seoul[16] corpus in terms of their static spectral characteristics. The plosives were automatically extracted by a Praat script. In order to estimate the percent correctness in the classification of the plosives, discriminant analyses were performed whose trainings were based on four spectral moments, i.e. the center of gravity, variance, skewness and kurtosis as suggested in [6]. Another set of discriminant analyses were performed based on the spectral tilts. In the last set of analyeses, the spectral moments and tilts were both used in the training. Results showed that the correct classification rate did not exceed around 65% in the best case, which suggested that phonetic cues other than the release burst would be necessary including the dynamic spectral aspects and vowel-onset cues.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.969-980
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• 2008

This paper presents the comparison of four methods, linear, quadratic, canonical and non-parametric discriminant analyses to discriminate the consumers' taste grade with sensory variables, such as tenderness, juiciness, flavor, and overall acceptability based on Consumer Sensory Survey. The classification ability of each method is measured and compared by the resubstitution error rate.

• Korean Journal of Pharmacognosy
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• v.44 no.4
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• pp.344-349
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• 2013

Near infrared spectroscopy (NIRS) combined with multivariate analysis was used to discriminate the geographical origin of Alisma orientale from Korea (n=94) and China (n=72). Two-thirds of samples were selected randomly for the training set, and one-third of samples for the test set. Second derivative was used for the pretreatment of NIR spectra. Partial least square discriminant analysis (PLS-DA) models correctly discriminated 100% of the Korean and Chinese A. orientale samples. These results demonstrate the potential use of NIR spectroscopy combined with multivariate analysis as a rapid and accurate method to discriminate A. orientale according to their geographical origin.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.31-34
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• 1996

The extensive researches have compared the performance of neural networks(NN) with those of various statistical techniques for the classification problem. The empirical results of these comparative studies have indicated that the neural networks often outperform the traditional statistical techniques. Moreover, there are some efforts that try to combine various classification methods, especially multivariate discriminant analysis with neural networks. While these efforts improve the performance, there exists a problem violating robust assumptions of multivariate discriminant analysis that are multivariate normality of the independent variables and equality of variance-covariance matrices in each of the groups. On the contrary, cluster analysis alleviates this assumption like neural networks. We propose a new approach to classification problems by combining the cluster analysis with neural networks. The resulting predictions of the composite model are more accurate than each individual technique.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.1
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• pp.16-21
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• 2001

전자 상거래에서 고객의 성향을 이해하기 위해서는 일반적으로 판매 실무에서의 경험과 전문적인 지식을 필요로 하게 된다. 데이터 마이닝은 고객들에 대한 데이터의 분석을 통해서 이러한 성향들을 알아내는 것을 목표로 한다. 베이지안 네트워크는 DAG(Directed Acyclic Graph)를 이용하여 데이터의 구조를 시각적으로 표현하여 주는 확률모형으로 변수사이의 종속관계를 밝히고 데이터 마이닝의 기법으로 이용할 수 있다. 본 논문에서는 베이지안 네트워크를 사용하여 전자 상거래 고객들의 성향을 분석하기 위한 방법을 제시한다. 또한 고객성향에 대한 주요 요인을 분석하기 위해 Discriminant 모형을 이용하고 그 유용성을 다른 방법들과 비교하였다.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.6
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• pp.631-642
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• 1998

Fall-out particles were collected by the modified British deposit gauges at 35 sampling sites in Suwon area from January to November, 1996. Twenty chemical species (Al. Ba, Cd, Cr, K, Pb, Sb, Zn, Cu, Fe, Ni, V, F-, Cl-, NO3-, 5042-, Na+, NH4+, Mg2+, and Ca2+) were analyzed by AAS and If. The purposes of this study were to estimate qualitatively various emission sources of the fell-out particle by applying multivariate statistical techniques such as factor analysis, multiple regression analysis, and discriminant analysis. During the study, outlier sites were determined by a z-score method. Cl-, Na+, Mg2+, and SO42- were highly correlated due to their common marine related source. Wind speed was the most influential factor for the deposition fluxes of the particle itself and all the chemical species as well. When applying the factor analysis, 8 source patterns were qualitatively obtained, such as marine source, soil source, oil burning source, Cr related source, tire source, Cd related source, agriculture source, and F- related source. As a result of the multiple regression analysis, we could suggest that some chemical compounds may possibly exist in the form of CaSO4, NaN03, NaCl, MgC12, (NH4)2SO4, NaF, and CaCl2 in the fall-out particles. Finally, spatial and seasonal classification study performed by a discriminant analysis showed th.at SO42-, Ca2+, Cl-, and Fe were dominant in the group of spatial pattern; however, SO42-, Cl-, Al, and V were in the group of seasonal pattern.

• Korean Chemical Engineering Research
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• v.48 no.4
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• pp.483-489
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• 2010

An image analysis-based soft sensor is designed and applied to automatic quality classification of product appearance with color-textural characteristics. In this work, multiresolutional multivariate image analysis (MR-MIA) is used in order to analyze product images with color as well as texture. Fisher's discriminant analysis (FDA) is also used as a supervised learning method for automatic classification. The use of FDA, one of latent variable methods, enables us not only to classify products appearance into distinct classes, but also to numerically and consistently estimate product appearance with continuous variations and to analyze characteristics of appearance. This approach is successfully applied to automatic quality classification of intermediate and final products in industrial manufacturing of engineered stone countertops.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.1
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• pp.138-143
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• 2011

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. As a part of researches for standardization and objectification of differentiation of syndromes for stroke, in this present study, we tried to develop the statistical diagnostic tool discriminating the 4 subtypes of syndrome differentiation using the essential indices considering the sex. Discriminant analysis was carried out using clinical data collected from 1,448 stroke patients who was identically diagnosed for the syndrome differentiation subtypes diagnosed by two clinical experts with more than 3 year experiences. Empirical discriminant model(V) for different sex was constructed using 61 significant symptoms and sign indices selected by stepwise selection. We comparison. We make comparison a between discriminant model(V) and discriminant model(IV) using 33 significant symptoms and sign indices selected by stepwise selection. Development of statistical diagnostic tool discriminating 4 subtypes by sex : The discriminant model with the 24 significant indices in women and the 19 significant indices in men was developed for discriminating the 4 subtypes of syndrome differentiation including phlegm-dampness, qi-deficiency, yin-deficiency and fire-heat. Diagnostic accuracy and prediction rate of syndrome differentiation by sex : The overall diagnostic accuracy and prediction rate of 4 syndrome differentiation subtypes using 24 symptom and sign indices was 74.63%(403/540) and 68.46%(89/130) in women, 19 symptom and sign indices was 72.05%(446/619) and 70.44%(112/159) in men. These results are almost same as those of that the overall diagnostic accuracy(73.68%) and prediction rate(70.59%) are analyzed by the discriminant model(IV) using 33 symptom and sign indices selected by stepwise selection. Considering sex, the statistical discriminant model(V) with significant 24 symptom and sign indices in women and 19 symptom and sign indices in men, instead of 33 indices would be used in the field of oriental medicine contributing to the objectification of syndrome differentiation with parsimony rule.

• Journal of Technology Innovation
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• v.14 no.1
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• pp.177-199
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• 2006

Recently, it has become important to establish a technology appraisal system because of increasing a service of technology credit guarantee. Also, there have been many efforts to evaluate a technology in the advanced countries. The technology/credit appraisal model which can measure firms' performance was suggested by Small and Medium Business Administration. In this paper, we analyze a structural validity of the model by confirmatory factor analysis and estimate the model which can distinguish whether an investment is possible or not by discriminant analysis.