일반화 가법 커널 정준상관분석을 이용한 다중그룹 분석

학위논문(석사)--서울대학교 대학원 :자연과학대학 통계학과,2019. 8. 임채영.Multivariate analysis methods have been widely used and one of popular methods is canonical correlation analysis (CCA). Despite several advantages of CCA, it has some limitations; restricted to linear relationship and two groups. To overcome such limitation, modi ed version of CCA have been proposed by several researchers, like kernel CCA and generalized CCA. In this paper, we propose an extension of CCA that allows multi-group and nonlinear relationship in additive fashion. We call our approach Generalized Additive Kernel Canonical Analysis (GAKCCA). In addition to exploring multi-group relationship with nonlinear extension, GAKCCA can reveal contribution of variables in each group; which enables in-depth structural analysis. Simulation study shows that GAKCCA can distinguish a relationship between groups and whether they are correlated or not.다양한 다변량 분석 방법들이 널리 쓰이고 있으며 그 중에 널리 쓰이고 있는 방법론 중에 하나로 정준상관분석 (Canonical correlation analysis, CCA)이 있다. 정준상관분석은 많은 장점에도 불구하고 선형관계에만 국한되었다는 점, 2개의 그룹에만 적용할 수 있는 점 등의 한계점을 지니고 있다. 이러한 한계를 극복하기 위해 커널 정준상관분석 (Kernel canonical correlation analysis, KCCA), 일반화 정준상관분석 등 여러 변형된 정준상관분석법들이 제안되었다. 본 논문에서는 새로운 모델인 일반화 가법 커널 정준상관분석 (Generalized additive kernel canonical correlation analysis, GAKCCA)를 제안하고자 한다. 비선형 확장을 통한 다중 그룹 사이의 관계를 분석하는 것과 더불어, 일반화 가법 커널 정준상관분석은 각 그룹 내 변수가 그룹간 관계에 어느 정도 기여하는지를 보여줄 수 있으며 이를 통해 심층적인 구조 분석이 가능하다. 또한 시뮬레이션 결과를 통해 일반화 가법 커널 정준상관분석이 다중 그룹들 간의 관계 여부를 나타낼 수 있다는 것을 보여준다.Chapter 1 Introduction 1 Chapter 2 Model 4 2.1 Canonical Correlation Analysis and its variants . . . . . . . . . . 4 2.2 Generalized Additive Kernel Canonical Correlation Analysis . . . 6 2.3 Regularization Parameter Selection . . . . . . . . . . . . . . . . . 13 2.4 Permutation test . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Chapter 3 Empirical Study 16 Chapter 4 Conclusion 22 Bibliography 24 국문초록 28Maste

Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength

The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Method: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. With an analytical solution of the product of Gaussian distribution, we adopted variational inference to train the designed MVAE-PoE model to perform common latent feature extraction. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. Results: The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Compared to existing multi-view information fusion methods, the proposed MVAE-PoE achieved the best performance. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation dosage and clinical costs from QCT.Comment: 16 pages, 3 figure

Validação de instrumento de medida de composição de carcaça, composição tecidual de cortes comerciais e qualidade de carne a partir de vídeo image analisys de carcaças de cordeiros.

CNPQObjetivou-se com este trabalho investigar se a Vídeo Image Analisys (VIA) de carcaças resfriadas, fornecem uma descrição consistente da qualidade de carne, considerando ainda as composições teciduais da carcaça e dos cortes comerciais. Informações de 67 carcaças frias de cordeiros machos castrados foram submetidas à correlação canônica regularizada generalizada (RGCCA) e à modelagem de equações estruturais via modelagem de quadrados mínimos parciais – modelagem de caminho (PLS-PM), avaliando blocos de variáveis manifestas (VM) de: forma da carcaça (SHAPE), qualidade da carne (QUALI_MEAT), composição tecidual da carcaça (TISSUE_CARCASS) e dos cortes comerciais (TISSUE_PRIMALCUTS); sendo cada bloco tratado como variável latente (VL). Foram avaliados três modelos teóricos que divergiam quanto a obtenção dos caracteres de forma: por morfometria (SHAPE_MPH) e por VIA (SHAPE_VIA1 e SHAPE_VIA2). Os modelos foram capazes de predizer características de qualidade de carne somente nos aspectos de cocção e força de cisalhamento. Todos os modelos atenderam aos critérios de validade convergente, confiabilidade composta, validade discriminante, validade preditiva e tamanho dos efeitos, mostrando altas acurácias na predição das VL’s, especialmente de QUALI_MEAT: 0,77; 0,82 e 0,78, em SHAPE_MPH, SHAPE_VIA1 e SHAPE_VIA2, respectivamente. Os modelos de VIA promoveram maiores coeficientes de determinação que a avaliação da morfometria da carcaça in situ (SHAPE_MPH), exceto para TISSUE_PRIMALCUTS. Os coeficientes de caminho da relação SHAPEQUALI_MEAT não foram significativos para todos os modelos. SHAPE_MPH e SHAPE_VIA1 foram considerados adequados e validados pelos procedimentos de qualidade de ajustes para modelagem de equações estruturais. O modelo SHAPE_VIA1 demonstrou correlações positivas e altas entre as VL’s: SHAPE_VIA1 e TISSUE_CARCASS (r =0,88), SHAPE_VIA1 e TISSUE_PRIMALCUTS (r = 0,81) e TISSUE_CARCASS e TISSUE_PRIMALCUTS (r = 0,81); e altamente negativas 6 entre QUALI_MEAT e TISSUE_PRIMAL CUTS (r = -0,86), QUALI_MEAT e TISSUE_CARCASS (r = -0,87). Altos escores de SHAPE_VIA1, TISSUE_CARCASS e TISSUE_PRIMAL CUTS representam, individualmente: carcaça ampla, maciça e circular; carcaça pesada com grande porção comestível, conformação e acabamento superiores; maior peso e porção comestível de cortes comerciais; respectivamente. Menores escores para QUALI_MEAT correspondem a carne suculenta e macia. Foi realizada uma Análise de Agrupamento Latente a partir das pontuações das VL’s, formando quatro clusters, onde o Cluster 1 (N=19) foi o que apresentou maiores escores de SHAPE_VIA1, TISSUE_CARCASS, TISSUE_PRIMALCUTS e menores escores para QUALI_MEAT e; Cluster 4 (N=15) – grupos com os menores escores de SHAPE_VIA1, TISSUE_CARCASS, TISSUE_PRIMALCUTS e maiores escores para QUALI_MEAT. Os Clusters 2 (N=17) e 3 (N=16) foram os grupos com escores intermediários para todas as VL’s. O instrumento proposto e validado permitiu, por meio dos perfis geométricos obtidos por processamento de imagem, obter associações mais eficientes com características quantitativas, teciduais e qualitativas da carcaça, demonstrando inter-relações entre os grupos de descritores de forma e composição, permitindo, por meio dos escores de variáveis latentes, estabelecer categorias para classificação de carcaça. Dessa forma, apresenta-se como uma metodologia consistente para ser incorporada em sistemas de visão computacional capazes de operacionalizar tais mensurações na imagem da carcaça de forma instantânea, fornecendo categorias de interesse à cadeia produtiva da ovinocultura de corte.The mean of the paper it was investigate if the vídeo image analisys (VIA) from cold carcass, provide a consistent description of the meat quality, whereas conposition tissues from carcass and primal cuts. Information of 67 cold carcass from lambs male nesteres was submited on the regularized generalizeted canonical correlation (RGCCA) and the structural modeling equation by PLS-PM, assessing blocks of the manifest variables (MV’s) of: SHAPE from carcass (SHAPE), meat quality (QUALI_MEAT), tissue carcass composition (TISSUE_CARCASS), and the primal cuts (TISSUE_PRIMALCUTS), where it block was trated by a latent variable (LV’s). The teoric models diverged from the getting of the form characthers: by morphometric (SHAPE_MPH) and By VIA (SHAPE_VIA1 and SHAPE_VIA2). The models it was availabels to predict the of characteristic of the loss cooking and the Shearing force. All the models attended the criterion of convergente validity, composite realibity, predictivy validity and size of effect, demostrating high aacuracy on the prediction of the LV’s specialy of the QUALI_MEAT: 0.77, 0.82 and 0.78, and SHAPE_MPH, SHAPE_VIA1 and SHAPE_VIA2 respectivily. The models of VIA promoted biggers coeficientes of determination from the morphometric avaliation on the carcass in situ, except for the TISSUE_PRIMACUTS. The path coeficientes of the relation Shape relation SHAPEQUALI_MEAT, were not significantily for the models SHAPE_MPH AND SHAPE_VIA1 was considerated suitable and validated by the procedures of quality of adjustment for the structural equation modelation. The model SHAPE_VIA1 demostrated positive and high correlation between the LV’s: SHAPE_VIA1 and the TISSUE_CARCASS (r = 0.88), SHAPE_VIA1 and TISSUE_PRIMALCUTS (r= 0.81) and negative batween QUALI_MEAT and TISSUE_PRIMALCUTS (r=-0.86), QUALI_MEAT and TISSUE_CARCASS (r=-0.87), represented meat and fatness, greater weight and edible portion of comercial cuts, respctively. Lower scores for QUALI_MEAT corresponding to juicy and tender meat. A latente grouping analisys was performed from the LV’s scores, forming four cluster, where cluster 1 presented the higher scores of SHAPE_VIA1, TISSUE_CARCASS, TISSUE_PRIMALCUTS and lower scores for QUALI_MEAT. Cluster 4 groups with the lower scores of SHAPE_VIA1, TISSUE_CARCASS, TISSUE_PRIMALCUTS and higher scores for QUALI_MEAT. Cluster 2 and 3 where the groups with intermediate scores for all LV’s. The proposed and validated instrument allowed, through the geometric profiles obtained by image processing, to obtain more efficient associations with quantitative, tissue and qualitative characteristics of the carcass, 33 demonstrating interrelations between the groups of descriptors of form and composition, allowing, through the latent variable scores, establish categories for carcass classification. Thus, it is presented as a consistent methodology to be incorporated into computer vision systems capable of operationalizing such measurements in the carcass image in an instantaneous way, providing categories of interest to the production chain of the cutting sheep

Kernel Generalized Canonical Correlation Analysis

International audienceThere is a growing need to analyze datasets characterized by several sets of variables observed on a single set of observations. Such complex but structured dataset are known as multiblock dataset, and their analysis requires the development of new and flexible tools. For this purpose, Kernel Generalized Canonical Correlation Analysis (KGCCA) is proposed and offers a general framework for multiblock data analysis taking into account an a priori graph of connections between blocks. It appears that KGCCA subsumes, with a single monotonically convergent algorithm, a remarkably large number of well-known and new methods as particular cases. KGCCA is applied to a simulated 33-block dataset and a real molecular biology dataset that combines Gene Expression data, Comparative Genomic Hybridization data and a qualitative phenotype measured for a set of 5353 children with glioma.KGCCA is available on CRAN as part of the RGCCA package

Kernel Generalized Canonical Correlation Analysis

A classical problem in statistics is to study relationships between several blocks of variables. The goal is to find variables of one block directly related to variables of other blocks. The Regularized Generalized Canonical Correlation Analysis (RGCCA) is a very attractive framework to study such a kind of relationships between blocks. However, RGCCA captures linear relations between blocks and to assess nonlinear relations we propose a kernel extension of RGCCA