BIGDATA: Mid-Scale: DA: Collaborative Research: Big Tensor Mining: Theory, Scalable Algorithms and Applications

BIGDATA：中型：DA：协作研究：大张量挖掘：理论、可扩展算法和应用

• 批准号: 1247489
• 负责人: Christos Faloutsos
• 金额: $ 89.49万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-12-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247489&HistoricalAwards=false
• 关键词: BIGDATA; Mid; Scale; DA; Collaborative

Tensors are multi-dimensional generalizations of matrices, and so can have non-numeric entries. Extremely large and sparse coupled tensors arise in numerous important applications that require the analysis of large, diverse, and partially related data. The effective analysis of coupled tensors requires the development of algorithms and associated software that can identify the core relations that exist among the different tensor modes, and scale to extremely large datasets. The objective of this project is to develop theory and algorithms for (coupled) sparse and low-rank tensor factorization, and associated scalable software toolkits to make such analysis possible. The research in the project is centered on three major thrusts. The first is designed to make novel theoretical contributions in the area of coupled tensor factorization, by developing multi-way compressed sensing methods for dimensionality reduction with perfect latent model reconstruction. Methods to handle missing values, noisy input, and coupled data will also be developed. The second thrust focuses on algorithms and scalability on modern architectures, which will enable the efficient analysis of coupled tensors with millions and billions of non-zero entries, using the map-reduce paradigm, as well as hybrid multicore architectures. An open-source coupled tensor factorization toolbox (HTF- Hybrid Tensor Factorization) will be developed that will provide robust and high-performance implementations of these algorithms. Finally, the third thrust focuses on evaluating and validating the effectiveness of these coupled factorization algorithms on a NeuroSemantics application whose goal is to understand how human brain activity correlates with text reading & understanding by analyzing fMRI and MEG brain image datasets obtained while reading various text passages.Given triplets of facts (subject-verb-object), like ('Washington' 'is the capital of' 'USA'), can we find patterns, new objects, new verbs, anomalies? Can we correlate these with brain scans of people reading these words, to discover which parts of the brain get activated, say, by tool-like nouns ('hammer'), or action-like verbs ('run')? We propose a unified "coupled tensor" factorization framework to systematically mine such datasets. Unique challenges in these settings include (a) tera- and peta-byte scaling issues, (b) distributed fault-tolerant computation, (c) large proportions of missing data, and (d) insufficient theory and methods for big sparse tensors. The Intellectual Merit of this effort is exactly the solution to the above four challenges.The Broader Impact is the derivation of new scientific hypotheses on how the brain works and how it processes language (from the never-ending language learning (NELL) and NeuroSemantics projects) and the development of scalable open source software for coupled tensor factorization. Our tensor analysis methods can also be used in many other settings, including recommendation systems and computer-network intrusion/anomaly detection.KEYWORDS:Data mining; map/reduce; read-the-web; neuro-semantics; tensors.

张量是矩阵的多维概括，因此可以具有非数字条目。在需要分析大型，多样化和部分相关数据的许多重要应用中，出现了极大稀疏的耦合张量。对耦合张量的有效分析需要开发算法和相关软件，这些软件可以识别不同张量模式之间存在的核心关系，并扩展到非常大的数据集。该项目的目的是开发（耦合）稀疏和低量张量分解的理论和算法，以及相关的可扩展软件工具包，以使这种分析成为可能。该项目的研究集中在三个主要推力上。第一个旨在通过开发多道路压缩感测方法来减少尺寸，以通过完美的潜在模型重建来开发多路压缩传感方法，从而在耦合张量分解方面做出新的理论贡献。处理缺失值，嘈杂输入和耦合数据的方法也将开发。第二个推力着重于对现代体系结构的算法和可扩展性，这将使使用MAP-REDUCE范式以及混合多层架构进行有效分析具有数百万和数十亿个非零条目的耦合张量。将开发一个开源耦合张量分解工具箱（HTF-混合张量分解），该工具箱将提供这些算法的强大且高性能的实现。最后，第三个推力重点是评估和验证这些耦合分解算法在神经学应用程序上的有效性我们发现模式，新对象，新动词，异常？我们可以将它们与阅读这些单词的人的脑部扫描相关联，以发现大脑的哪些部分被类似工具的名词（“ hammer'）或类似动作的动词（“ run un un un”）激活？我们提出了一个统一的“耦合张量”分解框架，以系统地挖掘此类数据集。这些设置中的独特挑战包括（a）TERA和PETA-BYTE缩放问题，（b）分布式耐故障计算，（c）大量丢失的数据，以及（d）大稀疏张量的理论和方法不足。这项工作的智力优点正是解决上述四个挑战的解决方案。更广泛的影响是推导有关大脑如何运作的新科学假设以及如何处理语言（从永无止境的语言学习（NELL）和神经使用者项目）以及开发可扩展的开源软件来开发构造张力分解的可扩展开源软件。我们的张量分析方法也可以用于许多其他设置，包括建议系统和计算机网络入侵/异常检测。关键单词：数据挖掘；地图/减少；阅读 - 网络；神经仪式；张量。