• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.265-271
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• 2008

It is commonly believed that diseases of human or economic traits of livestock are caused not by single genes acting alone, but multiple genes interacting with one another. This issue is difficult due to the limitations of parametric-statistic methods of gene effects. So we introduce multifactor-dimensionality reduction(MDR) as a methods for reducing the dimensionality of multilocus information. The MDR method is nonparametric (i. e., no hypothesis about the value of a statistical parameter is made), model free (i. e., it assumes no particular inheritance model) and is directly applicable to case-control studies. Application of the MDR method revealed the best model with an interaction effect between the SNPs, SNP1 and SNP3, while only one main effect of SNP1 was statistically significant for LMA (p < 0.01) under a general linear mixed model.

• Genomics & Informatics
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• v.14 no.4
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.468-488
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• 2016

The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.49-58
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• 2004

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

• BMB Reports
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• v.42 no.9
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• pp.617-622
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• 2009

We investigated the genetic associations of ischemic stroke by identifying epistasis of its heterogeneous subtypes such as small vessel occlusion (SVO) and large artery atherosclerosis (LAA). Epistasis was analyzed with 24 genes in 207 controls and 271 patients (SVO = 110, LAA = 95) using multifactor dimensionality reduction and entropy decomposition. The multifactor dimensionality reduction analysis with any of 1- to 4-locus models showed no significant association with LAA (P > 0.05). The analysis of SVO, however, revealed a significant association in the best 3-locus model with P10L of TGF-$\beta{1}$, C1013T of SPP1, and R485K of F5 (testing balanced accuracy = 63.17%, P < 0.05). Subsequent entropy analysis also revealed that such heterogeneity was present and quite a large entropy was estimated among the 3 loci for SVO (5.43%), but only a relatively small entropy was estimated for LAA (1.81%). This suggests that the synergistic epistasis model might contribute specifically to the pathogenetsis of SVO, which implies a different etiopathogenesis of the ischemic stroke subtypes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Convergence Security Journal
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• v.23 no.1
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• pp.117-123
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• 2023

To detect advanced ransomware attacks with machine learning-based models, the classification model must train learning data with high-dimensional feature space. And in this case, a 'curse of dimension' phenomenon is likely to occur. Therefore, dimensionality reduction of features must be preceded in order to increase the accuracy of the learning model and improve the execution speed while avoiding the 'curse of dimension' phenomenon. In this paper, we conducted classification of ransomware by applying three machine learning models and two feature extraction techniques to two datasets with extremely different dimensions of feature space. As a result of the experiment, the feature dimensionality reduction techniques did not significantly affect the performance improvement in binary classification, and it was the same even when the dimension of featurespace was small in multi-class clasification. However, when the dataset had high-dimensional feature space, LDA(Linear Discriminant Analysis) showed quite excellent performance.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.959-971
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• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.381-393
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• 2005

As a promising technique for dimension reduction in regression analysis, Sliced Inverse Regression (SIR) and an associated chi-square test for dimensionality were introduced by Li (1991). However, Li's test needs assumption of Normality for predictors and found to be heavily dependent on the number of slices. We will provide a unified asymptotic test for determining the dimensionality of the SIR model which is based on the probabilistic principal component analysis and free of normality assumption on predictors. Illustrative results with simulated and real examples will also be provided.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.6
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• pp.784-788
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• 2008

Studies to detect genes responsible for economic traits in farm animals have been performed using parametric linear models. A non-parametric, model-free approach using the 'expanded multifactor-dimensionality reduction (MDR) method' considering high dimensionalities of interaction effects between multiple single nucleotide polymorphisms (SNPs), was applied to identify interaction effects of SNPs responsible for carcass traits in a Hanwoo beef cattle population. Data were obtained from the Hanwoo Improvement Center, National Agricultural Cooperation Federation, Korea, and comprised 299 steers from 16 paternal half-sib proven sires that were delivered in Namwon or Daegwanryong livestock testing stations between spring of 2002 and fall of 2003. For each steer at approximately 722 days of age, the Longssimus dorsi muscle area (LMA) was measured after slaughter. Three functional SNPs (19_1, 18_4, 28_2) near the microsatellite marker ILSTS035 on BTA6, around which the QTL for meat quality were previously detected, were assessed. Application of the expanded MDR method revealed the best model with an interaction effect between the SNPs 19_1 and 28_2, while only one main effect of SNP19_1 was statistically significant for LMA (p<0.01) under a general linear mixed model. Our results suggest that the expanded MDR method better identifies interaction effects between multiple genes that are related to polygenic traits, and that the method is an alternative to the current model choices to find associations of multiple functional SNPs and/or their interaction effects with economic traits in livestock populations.