• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.761-764
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• 2004

얼굴 데이터를 사용하는 인식 시스템에서 특징 벡터의 차원은 일반적으로 매우 크다. 패턴인식에서 차원 축소는 중요한 문제로서, 효과적인 얼굴 인식을 위한 특징 벡터의 차원 축소는 필수적이라 할 수 있다. 본 논문에서는 획득된 얼굴 데이터로부터 저 차원의 강건한 특징을 얻기 위하여 웨이블릿을 사용하고, 식별력 있는 특징을 얻기 위하여 direct linear discriminant analysis를 사용하였다. Direct linear discriminant analysis 방법을 사용하기 이전에 웨이블릿을 사용함으로써 계산 복잡도를 줄여줄 뿐만 아니라 식별력을 높여주고 효과적으로 얼굴 데이터의 차원을 축소할 수 있음을 보여 준다. 얼굴의 패턴정합을 위해서는 최근접 평균 분류기(Nearest Mean Classifier)를 사용하였으며, 최근접 평균 분류기를 사용함으로써 분류를 위한 시간을 최소화하였다. 본 논문에서 인간의 얼굴인식을 위해 제시한 방법이 얼굴패턴을 표현하는 효과적인 방법이며, 시간 및 공간의 절약이라는 측면에서 유리하다는 것을 보여준다.

• ETRI Journal
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• v.41 no.5
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• Bulletin of the Korean Chemical Society
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• v.9 no.4
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• pp.223-231
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• 1988

Some pattern recognition methods have been used to characterize Korean ancient porcelainsherds using their elemental composition as analyzed by instrumental neutron activation analysis. A combination of analytical data by means of statistical linear discriminant analysis(SLDA) has resulted in removal of redundant variables, optimal linear combination of meaningful variables and formulation of classification rules. The plot in the first-to-second discriminant scores has shown that the three distinct territorial regions exist among porcelainsherds of Kyungki, Chunbuk-Chungnam, and Chunnam, with respective efficiencies of 20/30, 22/27 and 14/15. Similar regions have been found to exist among punchong porcelain and ceradonsherds of Kyungki, Chungnam and Chunbuk, with respective efficiencies of 7/9, 15/16 and 6/6. Classification has been further attempted by statistical isolinear multiple component analysis(SIMCA), using the sample set selected appropriately through SLDA as training set. For this purpose, all analytical data have been used. An agreement has generally been found between two methods, i.e., SLDA and SIMCA.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.31-39
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• 1995

In this paper, we have constructed phoneme discriminant filters [PDF] according to the linear discriminant function. These discriminant filters do not follow the heuristic rules by the experts but the mathematical methods in iterative learning. Proposed system. is based on the piecewise linear classifier and error correction learning method. The segmentation of speech and the classification of phoneme are carried out simutaneously by the PDF. Because each of them operates independently, some speech intervals may have multiple outputs. Therefore, we introduce the unified coefficients by the output unification process. But sometimes the output has a region which shows no response, or insensitive. So we propose time windows and median filters to remove such problems. We have trained this system with the 549 monosyllables uttered 3 times by 3 male speakers. After we detect the endpoint of speech signal using threshold value and zero crossing rate, the vowels and consonants are separated by the PDF, and then selected phoneme passes through the following PDF. Finally this system unifies the outputs for competitive region or insensitive area using time window and median filter.

• Management & Information Systems Review
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• v.35 no.3
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• pp.95-113
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• 2016

The study examines the question of what discriminant factors may affect differences between two groups classified by researchers' satisfaction with and continuous use intention of genetic resources(microorganisms). Survey data from researchers who are using microorganisms from a gene bank was used to empirically test. The survey, covering 150 researchers, was conducted from March 26 through April 17 2015. Linear discriminant analysis was used to test the research questions described in the study. Results from the tests show that utilization value and suitability of genetic resources for researchers' R&D activities play key roles in discriminating between the two groups classified by researchers' satisfaction with and continuous use intention of genetic resources, relatively lower and higher groups. The results indicate that useful trait information of and degree in promotion of researches by genetic resources appear to be weak in discriminating between the two groups, and that novelty of genetic resources does not play a crucial role in making a distinction between the two groups. We propose some policy implications based on the results of the study.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1044-1051
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• 2002

This paper proposes a two-phase mathematical programming approach by considering classification gap to solve the proposed credit scoring problem so as to complement any theoretical shortcomings. Specifically, by using the linear programming (LP) approach, phase 1 is to make the associated decisions such as issuing grant of credit or denial of credit to applicants. or to seek any additional information before making the final decision. Phase 2 is to find a cut-off value, which minimizes any misclassification penalty (cost) to be incurred due to granting credit to 'bad' loan applicant or denying credit to 'good' loan applicant by using the mixed-integer programming (MIP) approach. This approach is expected to and appropriate classification scores and a cut-off value with respect to deviation and misclassification cost, respectively. Statistical discriminant analysis methods have been commonly considered to deal with classification problems for credit scoring. In recent years, much theoretical research has focused on the application of mathematical programming techniques to the discriminant problems. It has been reported that mathematical programming techniques could outperform statistical discriminant techniques in some applications, while mathematical programming techniques may suffer from some theoretical shortcomings. The performance of the proposed two-phase approach is evaluated in this paper with line data and loan applicants data, by comparing with three other approaches including Fisher's linear discriminant function, logistic regression and some other existing mathematical programming approaches, which are considered as the performance benchmarks. The evaluation results show that the proposed two-phase mathematical programming approach outperforms the aforementioned statistical approaches. In some cases, two-phase mathematical programming approach marginally outperforms both the statistical approaches and the other existing mathematical programming approaches.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.141-149
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• 2004

The various methods have been studied to develop discriminant model for Pregnancy Induced Hypertension(PIH) as high risk pregnant. In this study, we adapt the approximate entropy which is the non-linear chaotic measuring method. Then, we develop the system to discriminant PIH pregnant using QUEST with S-PLUS.

• The Korean Journal of Applied Statistics
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• v.5 no.1
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• pp.103-111
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• 1992

The projection pursuit method has been gussested as a technique for the analysis of the multivariate data. This method seeks out interesting linear projections of the multivariate data onto a line of a plane to solve the curse or dimensionality. In this paper we developed the discriminant analysis by using the projection method and simulations were used for comparison between this and other existing discriminant analysis methods.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.35-41
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• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.