Statistical Analysis and Data Mining最新文献_第10页

Maximum Likelihood Estimation Over Directed Acyclic Gaussian Graphs. 有向无环高斯图上的最大似然估计。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2012-12-01 DOI: 10.1002/sam.11168

Yiping Yuan, Xiaotong Shen, Wei Pan

引用次数: 0

Multiple Response Regression for Gaussian Mixture Models with Known Labels. 具有已知标签的高斯混杂模型的多重响应回归。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2012-12-01 DOI: 10.1002/sam.11158

Wonyul Lee, Ying Du, Wei Sun, D Neil Hayes, Yufeng Liu

{"title":"Multiple Response Regression for Gaussian Mixture Models with Known Labels.","authors":"Wonyul Lee, Ying Du, Wei Sun, D Neil Hayes, Yufeng Liu","doi":"10.1002/sam.11158","DOIUrl":"10.1002/sam.11158","url":null,"abstract":"Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"5 6","pages":""},"PeriodicalIF":1.3,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3885347/pdf/nihms539872.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32023141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonlinear Vertex Discriminant Analysis with Reproducing Kernels. 带再现核的非线性顶点判别分析

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2012-04-01 DOI: 10.1002/sam.11137

Tong Tong Wu, Yichao Wu

引用次数: 0

Reduced Rank Ridge Regression and Its Kernel Extensions. 简化秩岭回归及其核扩展。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2011-12-01 Epub Date: 2011-10-07 DOI: 10.1002/sam.10138

Ashin Mukherjee, Ji Zhu

引用次数: 53

Clustering Based on Periodicity in High-Throughput Time Course Data. 基于周期性的高通量时间课程数据聚类。

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2011-12-01 DOI: 10.1002/sam.10137

Anna J Blackstock, Amita K Manatunga, Youngja Park, Dean P Jones, Tianwei Yu

引用次数: 2

A Novel Support Vector Classifier for Longitudinal High-dimensional Data and Its Application to Neuroimaging Data. 用于纵向高维数据的新型支持向量分类器及其在神经影像数据中的应用

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2011-12-01 DOI: 10.1002/sam.10141

Shuo Chen, F DuBois Bowman

{"title":"A Novel Support Vector Classifier for Longitudinal High-dimensional Data and Its Application to Neuroimaging Data.","authors":"Shuo Chen, F DuBois Bowman","doi":"10.1002/sam.10141","DOIUrl":"10.1002/sam.10141","url":null,"abstract":"Recent technological advances have made it possible for many studies to collect high dimensional data (HDD) longitudinally, for example images collected during different scanning sessions. Such studies may yield temporal changes of selected features that, when incorporated with machine learning methods, are able to predict disease status or responses to a therapeutic treatment. Support vector machine (SVM) techniques are robust and effective tools well-suited for the classification and prediction of HDD. However, current SVM methods for HDD analysis typically consider cross-sectional data collected during one time period or session (e.g. baseline). We propose a novel support vector classifier (SVC) for longitudinal HDD that allows simultaneous estimation of the SVM separating hyperplane parameters and temporal trend parameters, which determine the optimal means to combine the longitudinal data for classification and prediction. Our approach is based on an augmented reproducing kernel function and uses quadratic programming for optimization. We demonstrate the use and potential advantages of our proposed methodology using a simulation study and a data example from the Alzheimer's disease Neuroimaging Initiative. The results indicate that our proposed method leverages the additional longitudinal information to achieve higher accuracy than methods using only cross-sectional data and methods that combine longitudinal data by naively expanding the feature space.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"4 6","pages":"604-611"},"PeriodicalIF":1.3,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4189187/pdf/nihms-629358.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32742225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Space-efficient tracking of persistent items in a massive data stream 大规模数据流中持久项的空间高效跟踪

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2011-07-11 DOI: 10.1145/2002259.2002294

Bibudh Lahiri, S. Tirthapura, J. Chandrashekar

引用次数: 19

Sequential Support Vector Regression with Embedded Entropy for SNP Selection and Disease Classification. 嵌入熵的序列支持向量回归用于SNP选择和疾病分类。

IF 2.1 4区数学

Statistical Analysis and Data Mining Pub Date : 2011-06-01 DOI: 10.1002/sam.10110

Yulan Liang, Arpad Kelemen

引用次数: 0

A Machine-Learning Approach to Detecting Unknown Bacterial Serovars. 检测未知细菌血清型的机器学习方法。

IF 2.1 4区数学

Statistical Analysis and Data Mining Pub Date : 2010-10-01 DOI: 10.1002/sam.10085

Ferit Akova, Murat Dundar, V Jo Davisson, E Daniel Hirleman, Arun K Bhunia, J Paul Robinson, Bartek Rajwa

{"title":"A Machine-Learning Approach to Detecting Unknown Bacterial Serovars.","authors":"Ferit Akova, Murat Dundar, V Jo Davisson, E Daniel Hirleman, Arun K Bhunia, J Paul Robinson, Bartek Rajwa","doi":"10.1002/sam.10085","DOIUrl":"10.1002/sam.10085","url":null,"abstract":"Technologies for rapid detection of bacterial pathogens are crucial for securing the food supply. A light-scattering sensor recently developed for real-time identification of multiple colonies has shown great promise for distinguishing bacteria cultures. The classification approach currently used with this system relies on supervised learning. For accurate classification of bacterial pathogens, the training library should be exhaustive, i.e., should consist of samples of all possible pathogens. Yet, the sheer number of existing bacterial serovars and more importantly the effect of their high mutation rate would not allow for a practical and manageable training. In this study, we propose a Bayesian approach to learning with a nonexhaustive training dataset for automated detection of unmatched bacterial serovars, i.e., serovars for which no samples exist in the training library. The main contribution of our work is the Wishart conjugate priors defined over class distributions. This allows us to employ the prior information obtained from known classes to make inferences about unknown classes as well. By this means, we identify new classes of informational value and dynamically update the training dataset with these classes to make it increasingly more representative of the sample population. This results in a classifier with improved predictive performance for future samples. We evaluated our approach on a 28-class bacteria dataset and also on the benchmark 26-class letter recognition dataset for further validation. The proposed approach is compared against state-of-the-art involving density-based approaches and support vector domain description, as well as a recently introduced Bayesian approach based on simulated classes.","PeriodicalId":48684,"journal":{"name":"Statistical Analysis and Data Mining","volume":"3 5","pages":"289-301"},"PeriodicalIF":2.1,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230886/pdf/nihms242307.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30319662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discriminative frequent subgraph mining with optimality guarantees 具有最优性保证的判别频繁子图挖掘

IF 1.3 4区数学

Statistical Analysis and Data Mining Pub Date : 2010-10-01 DOI: 10.1002/SAM.V3:5

Marisa Thoma, Hong Cheng, A. Gretton, Jiawei Han, H. Kriegel, Alex Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt

引用次数: 23