• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1446-1452
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• 2014

Existing LDA uses the transform matrix that maximizes distance between classes. So we have to convert from an image to one-dimensional vector as training vector. However, in 2D-LDA, we can directly use two-dimensional image itself as training matrix, so that the classification performance can be enhanced about 20% comparing LDA, since the training matrix preserves the spatial information of two-dimensional image. However 2D-LDA uses same calculation schema for transformation matrix and therefore both LDA and 2D-LDA has the heteroscedastic problem which means that the class classification cannot obtain beneficial information of spatial distances of class clusters since LDA uses only data correlation-based covariance matrix of the training data without any reference to distances between classes. In this paper, we propose a new method to apply training matrix of 2D-LDA by using WPS-LDA idea that calculates the reciprocal of distance between classes and apply this weight to between class scatter matrix. The experimental result shows that the discriminating power of proposed 2D-LDA with weighted between class scatter has been improved up to 2% than original 2D-LDA. This method has good performance, especially when the distance between two classes is very close and the dimension of projection axis is low.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.169-181
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• 2014

Marginalized random effects models (MREM) are commonly used to analyze longitudinal categorical data when the population-averaged effects is of interest. In these models, random effects are used to explain both subject and time variations. The estimation of the random effects covariance matrix is not simple in MREM because of the high dimension and the positive definiteness. A relatively simple structure for the correlation is assumed such as a homogeneous AR(1) structure; however, it is too strong of an assumption. In consequence, the estimates of the fixed effects can be biased. To avoid this problem, we introduce one approach to explain a heterogenous random effects covariance matrix using a modified Cholesky decomposition. The approach results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The interpretation of the parameters is sensible. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using this method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.3
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• pp.75-81
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• 2017

This paper estimate a desired signal in a correlation wireless communication. The transmitted signal is mixed with the information signal, interference, and noise in wireless channel, and it is incident on the receiver. In this paper, we apply MUSIC algorithm and sub-array method to recover the total rank of the correlation matrix in order to estimation a desired signal among receiving signals. Through simulation, we analyze to compare the proposed method with the classical MUSIC algorithm. As a result of the simulation, the proposed method improved the resolution about 10degrees compared to the conventional MUSIC algorithm. We prove the superiority of the proposed method for the desired signal estimation in correlation channel.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.505-509
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• 2011

This paper attempts to classify known facial expressions and to establish the correlations between two regions (eye + eyebrows and mouth) in identifying the six prototypic expressions. Covariance is used to describe region texture that captures facial features for classification. The texture captured exhibit the pattern observed during the execution of particular expressions. Feature matching is done by simple distance measure between the probe and the modeled representations of eye and mouth components. We target JAFFE database in this experiment to validate our claim. A high classification rate is observed from the mouth component and the correlation between the two (eye and mouth) components. Eye component exhibits a lower classification rate if used independently.

• The Journal of the Acoustical Society of Korea
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• v.11 no.1
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• pp.67-79
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• 1992

For effective on-line ARMA parameter estimation, a covariance type ARMA fast transversal filter (FTF) algorithm is presented. The proposed algorithm is a covariance type implementation of ELS(Extended Least Squares) estimator and it is a fast time update recursion which is based on the fact that the correlation matrix of ARMA model satisfies the shift invariance property in each sub-block. The geometric approach is used in the derivation of the proposed algorithm. It takes small computational burden of 13N+37 MADPR(Multiplication And Division Per Recursion). Also, AR and MA orders can be independetly and arbitrarily specified.

• The Journal of the Korean dental association
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• v.54 no.11
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• pp.850-864
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• 2016

The Generalized Estimating Equations (GEE) approach is a widely used statistical method for analyzing longitudinal data and clustered data in clinical studies. In dentistry, due to multiple outcomes obtained from one patient, the outcomes produced from an individual patient are correlated with one another. This study focused on the basic ideas of GEE and introduced the types of covariance matrix and working correlation matrix. The quasi-likelihood information criterion (QIC) and quasi-likelihood information criterion approximation ($QIC_u$) were used to select the best working correlation matrix and the best fitting model for the correlated outcomes. The purpose of this study is to show a detailed process for the GEE analysis using SPSS software along with an orthodontic miniscrew example, and to help understand how to use GEE analysis in dental research.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.201-210
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• 2015

Repeated measurements on each variables of interest often arise in bioscience or medical research. We need to account for correlations among repeated measurements to assess the correlation between two variables in the presence of replication. This paper reviews methods to estimate a correlation coefficient between two variables in repeated measurements using the variance-covariance matrix of linear mixed effect models. We analyze acoustic radiation force impulse imaging (ARFI) data to assess correlation between three shear wave velocity (SWV) measurements in liver or spleen and spleen length by ultrasonography. We present how to obtain parameter estimates for the variance-covariance matrix and correlations in mixed effects models using PROC MIXED in SAS.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.5
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• pp.375-382
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• 2018

To eliminate strong jamming signals, a radar acquires a relatively weak target signal by using a side-lobe canceller (SLC) algorithm. This paper presents a novel adaptive SLC algorithm that is applicable to a fully digital active array radar. First, a covariance matrix is obtained from the SLC beam. Then, an adaptive SLC coefficient is extracted after calculating the correlation matrix between the main beam signal and the SLC beam signal. Finally, the target signal is estimated and the jamming signal is removed through the operation with the main beam signal. The application results from simulated radar signals demonstrated that the proposed algorithm is effective in an SLC system. Moreover, we analyzed various considerations and improved systematic usability.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1025-1038
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• 2014

Linear mixed model has often been utilized for genetic association analysis with family-based samples. The correlation matrix for family-based samples is constructed with kinship coefficient and assumes that parental phenotypes are independent and the amount of correlations between parent and offspring is same as that of correlations between siblings. However, for instance, there are positive correlations between parental heights, which indicates that the assumption for correlation matrix is often violated. The statistical validity and power are affected by the appropriateness of assumed variance covariance matrix, and in this thesis, we provide the linear mixed model with flexible variance covariance matrix. Our results show that the proposed method is usually more efficient than existing approaches, and its application to genome-wide association study of body mass index illustrates the practical value in real data analysis.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.329-336
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• 2006

In this paper, we propose a method of significant term extraction within a document. The technique used is Principal Component Analysis(PCA) which is one of the multivariate analysis methods. PCA can sufficiently use term-term relationships within a document by term-term correlations. We use a correlation matrix instead of a covariance matrix between terms for performing PCA. We also try to find out thresholds of both the number of components to be selected and correlation coefficients between selected components and terms. The experimental results on 283 Korean newspaper articles show that the condition of the first six components with correlation coefficients of |0.4| is the best for extracting sentence based on the significant selected terms.