Collaborative Research: Matrix-Model Machine Learning: Unifying Machine Learning and Scientific Computing

协作研究：矩阵模型机器学习：统一机器学习和科学计算

• 批准号: 0830780
• 负责人: Chris Ding
• 金额: $ 10万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830780&HistoricalAwards=false
• 关键词: Collaborative; Research; Matrix; Model; Machine

Collaborative Research: Matrix-Model Machine Learning: unifying machine learning and scientific computingTo analyze the ever-growing massive quantities of data for pattern recognition and knowledge discovery, effective machine learning models and efficient computational algorithms are essential tools. The goal of this research is to establish a theoretical foundation for solve challenging machine learning problems utilizing matrix/tensor computational methodologies, leveraging over the success of scientific computing over recent decades - including well-developed algorithms and mature, freely-available software.This research begins with a critical connection between machine learning and scientific computing: an effective global solution to K-means clustering algorithm is provided by the principal component analysis which is based on singular value decomposition (SVD). This fundamental relationship will be systematically extended to matrices, tensors and multi-relational data, to deal with increasingly higher dimensions, multiple indexes and data types. The key goal of this research is to establish that well-known scientific computing techniques such as SVD, matrix and tensor decompositions can be directly utilized for pattern discovery, and further develop these computational methodologies for semi-supervised learning, clustering and classification. The focus will be on multi-index data (tensors, such as a sequence of weather maps or a sequence of traffics over a network) and multi-relational data (multiple pairwise relations, such as protein domains ? proteins ? pathways or words ? documents ? authors). Applications in genomics, text mining, and computer vision will be investigated.

协作研究：矩阵模型机器学习：统一机器学习和科学计算，以分析不断增长的大量数据，以进行模式识别和知识发现，有效的机器学习模型和有效的计算算法是必不可少的工具。 这项研究的目的是建立一个理论基础，以解决利用矩阵/张量计算方法的挑战性机器学习问题，利用近几十年来科学计算的成功来利用科学计算的成功，包括算法和成熟的算法和可自由的软件。基于单数值分解（SVD）的组件分析。这种基本关系将系统地扩展到矩阵，张量和多关系数据，以处理越来越高的维度，多个索引和数据类型。这项研究的关键目的是确定可以将众所周知的科学计算技术（例如SVD，矩阵和张量分解）直接用于模式发现，并进一步开发这些计算方法，用于半手不见的学习，聚类和分类。重点将放在多指数数据（张量，例如一系列天气图或网络上的一系列运输序列）和多关系数据（多个成对关系，例如蛋白质域？蛋白质？蛋白质？pathways或单词？文档？作者？作者）。将研究基因组学，文本挖掘和计算机视觉的应用。