期刊论文详细信息
Statistical Analysis and Data Mining | |
Multi‐view predictive partitioning in high dimensions | |
Giovanni Montana1  Brian McWilliams1  | |
[1] Statistics Section, Department of Mathematics, Imperial College London, London, UK | |
关键词: multi‐; view clustering; predictive partitioning; partial least squares; subspace learning; PRESS statistic; web data mining; | |
DOI : 10.1002/sam.11144 | |
学科分类:社会科学、人文和艺术(综合) | |
来源: John Wiley & Sons, Inc. | |
【 摘 要 】
Abstract Many modern data mining applications are concerned with the analysis of datasets in which the observations are described by paired high-dimensional vectorial representations or `views'. Some typical examples can be found in web mining and genomics applications. In this article we present an algorithm for data clustering with multiple views, multi-view predictive partitioning (MVPP), which relies on a novel criterion of predictive similarity between data points. We assume that, within e.
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO201904047452844ZK.pdf | 49KB | download |