BIGDATA: Mid-Scale: DA : Collaborative Research Big Tensor Mining Theory

描述（由申请人提供）：给定三个事实（主语-动词-宾语），比如（“华盛顿”是“美国”的首都），我们能找到模式、新宾语、新动词、异常吗？我们能否通过大脑扫描来发现大脑的哪些部分被激活，比如说，被工具类名词（“锤子”）或动作类动词（“跑”）激活？我们提出了三个研究重点：i）激励应用：现实世界的万亿级应用，包括“阅读网络”，NeuroSemanticlarge推荐系统ii）大稀疏张量和耦合张量/矩阵分解的新理论和方法; iii）可扩展到万亿字节和千万亿字节的数据，使用map-reduce范式并将其扩展到多核设置。智力优势。我们将首先解决张量和耦合张量/矩阵分解的可扩展性问题。我们将利用在Hadoop上运行的Pegasusmining系统，我们将仔细利用稀疏性来避免中间数据爆炸。在理论/方法方面，我们提出了一个全新的多路压缩感知框架的张量和耦合张量/矩阵数据，（a）显着redijes复杂性和内存需求，（B）是服从映射减少和多核计算和（c）允许原则性填补缺失值。在上述应用的强烈推动下，并受其需求的影响，我们的原则性和可扩展的算法将使新的发现成为可能。教育：CS和ECE的研究生将在两个学科的交汇处接受跨学科主题的培训。PI具有成功合作的记录，co-PI汇集了理论，应用和软件开发（METIS）方面的互补优势。他们也经常涉及女性博士。学生和本科生在他们的研究。相关性（参见说明）：拟议中的研究旨在通过将阅读过程中的fMRI和MEG人脑活动与正在阅读的单词和短语的大规模语料库统计数据联系起来，更好地理解人类语言处理。关注可扩展性，我们将研究大型数据集，这是典型的，当前方法的能力之外。

DESCRIPTION (provided by applicant): 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 with brain scans, to discover which parts of the brain get activated, say, by tool-like nouns ('hammer'), or action-like verbs ('run')? We propose three research thrusts: i) Motivating Applications: Real-world tera-scale applications, including 'read-the-web', NeuroSemanticslarge recommendation systems ii) New theory and methods for big sparse tensor and coupled tensor/matrix factorization; and iii) Scalability to tera- and peta-byte data, using the map-reduce paradigm and extending it to multi-core settings. Intellectual Merit. We will be the first to address scalability issues for tensor and coupled tensor/matrix factorizations. We will leverage our Pegasusmining system which runs on Hadoop, and we will carefully exploit sparsity to avoid intermediate data explosion. On the theory/methods side, we propose a brand new multi-way compressed sensing framework for tensors and coupled tensor/matrix data that (a) dramatically redijces complexity and memory requirements, (b) is amenable to map-reduce and multi-core computation and (c) allows principled imputation of missing values. Heavily motivated by the above applications and influenced by their needs, our principled and scalable algorithms will enable new discoveries. Education: Graduate students in CS and ECE will be trained in a cross-disciplinary topic at the confluence of the two disciplines. The PIs have a record of successful collaboration, and the co-PIs bring together complementary strengths in theory, applications, and software development (METIS). They have also routinely involved female Ph.D. students and undergraduates in their research. RELEVANCE (See instructions): The proposed research seeks to develop a better understanding of human language processing, by relating fMRI and MEG human brain activity during reading to very large scale corpus statistics of the words and phrases being read. Focusing on scalability, we will study large datasets, which are outside the capabilities of typical, current methods.