• Title, Summary, Keyword: linear discriminant analysis

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Design of Pattern Classification Rule based on Local Linear Discriminant Analysis Classifier by using Differential Evolutionary Algorithm (차분진화 알고리즘을 이용한 지역 Linear Discriminant Analysis Classifier 기반 패턴 분류 규칙 설계)

  • Roh, Seok-Beom;Hwang, Eun-Jin;Ahn, Tae-Chon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.1
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    • pp.81-86
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    • 2012
  • In this paper, we proposed a new design methodology of a pattern classification rule based on the local linear discriminant analysis expanded from the generic linear discriminant analysis which is used in the local area divided from the whole input space. There are two ways such as k-Means clustering method and the differential evolutionary algorithm to partition the whole input space into the several local areas. K-Means clustering method is the one of the unsupervised clustering methods and the differential evolutionary algorithm is the one of the optimization algorithms. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.

Discrimination between earthquake and explosion by using seismic spectral characteristics and linear discriminant analysis (지진파 스펙트럼특성과 선형판별분석을 이용한 자연지진과 인공지진 식별)

  • 제일영;전정수;이희일
    • Proceedings of the Earthquake Engineering Society of Korea Conference
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    • pp.13-19
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    • 2003
  • Discriminant method using seismic signal was studied for discrimination of surface explosion. By means of the seismic spectral characteristics, multi-variate discriminant analysis was performed. Four single discriminant techniques - Pg/Lg, Lg1/Lg2, Pg1/Pg2, and Rg/Lg - based on seismic source theory were applied to explosion and earthquake training data sets. The Pg/Lg discriminant technique was most effective among the four techniques. Nevertheless, it could not perfectly discriminate the samples of the training data sets. In this study, a compound linear discriminant analysis was defined by using common characteristics of the training data sets for the single discriminants. The compound linear discriminant analysis was used for the single discriminant as an independent variable. From this analysis, all the samples of the training data sets were correctly discriminated, and the probability of misclassification was lowered to 0.7%.

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Datawise Discriminant Analysis For Feature Extraction (자료별 분류분석(DDA)에 의한 특징추출)

  • Park, Myoung-Soo;Choi, Jin-Young
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.1
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    • pp.90-95
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    • 2009
  • This paper presents a new feature extraction algorithm which can deal with the problems of linear discriminant analysis, widely used for linear dimensionality reduction. The scatter matrices included in linear discriminant analysis are defined by the distances between each datum and its class mean, and those between class means and mean of whole data. Use of these scatter matrices can cause computational problems and the limitation on the number of features. In addition, these definition assumes that the data distribution is unimodal and normal, for the cases not satisfying this assumption the appropriate features are not achieved. In this paper we define a new scatter matrix which is based on the differently weighted distances between individual data, and presents a feature extraction algorithm using this scatter matrix. With this new method. the mentioned problems of linear discriminant analysis can be avoided, and the features appropriate for discriminating data can be achieved. The performance of this new method is shown by experiments.

Objective Cloud Type Classification of Meteorological Satellite Data Using Linear Discriminant Analysis (선형판별법에 의한 GMS 영상의 객관적 운형분류)

  • 서애숙;김금란
    • Korean Journal of Remote Sensing
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    • v.6 no.1
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    • pp.11-24
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    • 1990
  • This is the study about the meteorological satellite cloud image classification by objective methods. For objective cloud classification, linear discriminant analysis was tried. In the linear discriminant analysis 27 cloud characteristic parameters were retrieved from GMS infrared image data. And, linear cloud classification model was developed from major parameters and cloud type coefficients. The model was applied to GMS IR image for weather forecasting operation and cloud image was classified into 5 types such as Sc, Cu, CiT, CiM and Cb. The classification results were reasonably compared with real image.

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A Comparison of the Discrimination of Business Failure Prediction Models (부실기업예측모형의 판별력 비교)

  • 최태성;김형기;김성호
    • Journal of the Korean Operations Research and Management Science Society
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    • v.27 no.2
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    • pp.1-13
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    • 2002
  • In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

Some Diagnostic Results in Discriminant Analysis

  • Bae, Whasoo;Hwang, Soonyoung
    • Journal of the Korean Statistical Society
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    • v.30 no.1
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    • pp.139-151
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    • 2001
  • Although lots of works are done in influence diagnostics, results in the multivariate analysis are quite rare. One of recent works done by Fung(1995) is about the single case influence diagnostics in the linear discriminant analysis. In this paper we extend Fung's results to the multiple cases diagnostics which are necessary in the linear discriminant analysis for two reasons among others; First, the masking effect cannot be detected by single case diagnostics and secondly two populations are concerned in the discriminant analysis, i.e., influential cases can occur in one or both populations.

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Linear Discriminant Analysis in Agricultural Experiment (농업실험에서 직선분리함수의 이용)

  • Young-Am Chae
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.22 no.1
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    • pp.80-82
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    • 1977
  • Using head length and head width of two wheat monosomic lines linear discriminant function of these two variables was calculated and also illustrated how one can effectively classify unknown individuals into a correct group belonging by means of this linear discriminant function in reverse. Brief suggestion on the utilization of this analysis in genetics and breeding program was given.

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Generalization of Fisher′s linear discriminant analysis via the approach of sliced inverse regression

  • Chen, Chun-Houh;Li, Ker-Chau
    • Journal of the Korean Statistical Society
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    • v.30 no.2
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    • pp.193-217
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    • 2001
  • Despite of the rich literature in discriminant analysis, this complicated subject remains much to be explored. In this article, we study the theoretical foundation that supports Fisher's linear discriminant analysis (LDA) by setting up the classification problem under the dimension reduction framework as in Li(1991) for introducing sliced inverse regression(SIR). Through the connection between SIR and LDA, our theory helps identify sources of strength and weakness in using CRIMCOORDS(Gnanadesikan 1977) as a graphical tool for displaying group separation patterns. This connection also leads to several ways of generalizing LDA for better exploration and exploitation of nonlinear data patterns.

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The Enhanced Power Analysis Using Linear Discriminant Analysis (선형판별분석을 이용한 전력분석 기법의 성능 향상)

  • Kang, Ji-Su;Kim, HeeSeok;Hong, Seokhie
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.24 no.6
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    • pp.1055-1063
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    • 2014
  • Recently, various methods have been proposed for improving the performance of the side channel analysis using the power consumption. Of those method, waveform compression method applies to reduce the noise component in pre-processing step. In this paper, we propose the new LDA(Linear Discriminant Analysis)-based signal compression method finding unique feature vector. Through experimentations, we are comparing the proposed method with the PCA(Principal Component Analysis)-based method which has known for the best performance among existing signal compression methods.