• Title, Summary, Keyword: Dimensionality Reduction

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Asymptotic Test for Dimensionality in Probabilistic Principal Component Analysis with Missing Values

  • Park, Chong-sun
    • 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.

Dimensionality Reduction Using PCA for Edge Computing (Edge Computing 환경에서의 PCA를 이용한 Dimensionality 감축 기법)

  • Lim, Hwan-Hee;Kim, Se-Jun;Kim, Kyoung-Tae;Youn, Hee-Yong
    • Proceedings of the Korean Society of Computer Information Conference
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    • pp.95-96
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    • 2018
  • Edge Computing은 Cloud Computing의 단점을 보완하기 위해 등장 하였으나, 자원 제한을 가지고 있는 Edge Node에서 데이터 분석 및 처리해야 하는 문제점이 있다. 이를 해결하기 위해 K-means clustering 알고리즘과 PCA 기법을 이용해 차원 추축을 이용한 계산비용과 처리시간을 줄이는 기법을 제안하였다. PCA란, 차원 축소 및 데이터 압축에 사용되는 기계학습 알고리즘 중 하나이며, 데이터에서 중요한 정보만 추출해 차원을 줄일 수 있다. 이를 통해 제안한 기법이 기존의 Reduction first clustering second(RFCS) 기법에 비해 성능이 우수한 것을 확인할 수 있었다.

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Multifactor Dimensionality Reduction(MDR) Analysis by Dummy Variables (더미(dummy) 변수를 활용한 다중인자 차원 축소(MDR) 방법)

  • Lee, Jea-Young;Lee, Ho-Guen
    • The Korean Journal of Applied Statistics
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    • v.22 no.2
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    • pp.435-442
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    • 2009
  • Multiple genes interacting is a difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction(MDR) statistical method by dummy variables was applied to identify interaction effects of single nucleotide polymorphisms(SNPs) responsible for longissimus mulcle dorsi area(LMA), carcass cold weight(CWT) and average daily gain(ADG) in a Hanwoo beef cattle population.

Statistical Interaction for Major Gene Combinations (우수 유전자 조합 선별을 위한 통계적 상호작용 방법비교)

  • Lee, Jea-Young;Lee, Yong-Won;Choi, Young-Jin
    • The Korean Journal of Applied Statistics
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    • v.23 no.4
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    • pp.693-703
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    • 2010
  • Diseases of human or economical traits of cattles are occured by interaction of genes. We introduce expanded multifactor dimensionality reduction(E-MDR), dummy multifactor dimensionality reduction(D-MDR) and SNPHarvester which are developed to find interaction of genes. We will select interaction of outstanding gene combinations and select final best genotype groups.

Gene-gene interaction of CCND1, ESR1 and CDK7 on the risk of breast cancer detected by multifactor dimensionality reduction and logistic regression (유방암과 CCND1, ESR1, CDK7 유전자 다형성의 상호작용; 로지스틱 회귀분석과 multifactor dimensionality reduction(MDR)의 분석 비교)

  • Choe Ji-Yeop;Ritchie Marylyn D.;Motsinger Alison A.;Lee Gyeong-Mu;No Dong-Yeong;Yu Geun-Yeong;Moore Jason H.;Gang Dae-Hui
    • 대한예방의학회:학술대회논문집
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    • pp.40.1-40.1
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    • 2004
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A Real-Time Pattern Recognition for Multifunction Myoelectric Hand Control

  • Chu, Jun-Uk;Moon, In-Hyuk;Mun, Mu-Seong
    • 제어로봇시스템학회:학술대회논문집
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    • pp.842-847
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    • 2005
  • This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction virtual hand. From experimental results, we show that all processes, including virtual hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.

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A Method for Microarray Data Analysis based on Bayesian Networks using an Efficient Structural learning Algorithm and Data Dimensionality Reduction (효율적 구조 학습 알고리즘과 데이타 차원축소를 통한 베이지안망 기반의 마이크로어레이 데이타 분석법)

  • 황규백;장정호;장병탁
    • Journal of KIISE:Software and Applications
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    • v.29 no.11
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    • pp.775-784
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    • 2002
  • Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.

Multifactor Dimensionality Reduction (MDR) Analysis to Detect Single Nucleotide Polymorphisms Associated with a Carcass Trait in a Hanwoo Population

  • Lee, Jea-Young;Kwon, Jae-Chul;Kim, Jong-Joo
    • 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.

Identification of Stearoyl-CoA Desaturase (SCD) Gene Interactions in Korean Native Cattle Based on the Multifactor-dimensionality Reduction Method

  • Oh, Dong-Yep;Jin, Me-Hyun;Lee, Yoon-Seok;Ha, Jae-Jung;Kim, Byung-Ki;Yeo, Jung-Sou;Lee, Jea-Young
    • Asian-Australasian Journal of Animal Sciences
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    • v.26 no.9
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    • pp.1218-1228
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    • 2013
  • Fat quality is determined by the composition of fatty acids. Genetic relationships between this composition and single nucleotide polymorphisms (SNPs) in the stearoyl-CoA desaturase1 (SCD1) gene were examined using 513 Korean native cattle. Single and epistatic effects of 7 SNP genetic variations were investigated, and the multifactor dimensionality reduction (MDR) method was used to investigate gene interactions in terms of oleic acid (C18:1), mono-unsaturated fatty acids (MUFAs) and marbling score (MS). The g.6850+77 A>G and g.14047 C>T SNP interactions were identified as the statistically optimal combination (C18:1, MUFAs and MS permutation p-values were 0.000, 0.000 and 0.001 respectively) of two-way gene interactions. The interaction effects of g.6850+77 A>G, g.10213 T>C and g.14047 C>T reflected the highest training-balanced accuracy (63.76%, 64.70% and 61.85% respectively) and was better than the individual effects for C18:1, MUFAs and MS. In addition, the superior genotype groups were AATTCC, AGTTCC, GGTCCC, AGTCCT, GGCCCT and AGCCTT. These results suggest that the selected SNP combination of the SCD1 gene and superior genotype groups can provide useful inferences for the improvement of the fatty acid composition in Korean native cattle.

The study on risk factors for diagnosis of metabolic syndrome and odds ratio using multifactor dimensionality reduction method (다중인자 차원 축소 방법에 의한 대사증후군의 위험도 분석과 오즈비)

  • Jin, Mi-Hyun;Lee, Jea-Young
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.4
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    • pp.867-876
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    • 2013
  • Metabolic syndrome has been known as a major factor of cardiovascular disease. Several metabolic disorders, particularly chronic disease is complex, and from individuals that appear in our country, the prevalence of the metabolic syndrome is increasing gradually. Therefore, this study, using a multi-factor dimensionality reduction method, checks the major single risk factor of metabolic syndrome and suggests a new diagnosis results of metabolic syndrome. Data of 3990 adults who responded to all the questionnaires of health interview are used from the database of the 5th Korea national health and nutrition examination survey conducted in 2010. As the result, the most dangerous single risk factor for metabolic syndrome was waist circumference and the most dangerous combination factors were waist circumference, triglyceride, and hypertension. This is the result of a new diagnosis of the metabolic syndrome. Especially, waist circumference, low HDL-cholesterol and hypertension were the most dangerous combination for male. In particular, the combination of waist circumference, triglyceride and diabetes was dangerous for obese people.