BIGDATA: Collaborative Research: F: Efficient and Exact Methods for Big Data Reduction

BIGDATA：协作研究：F：大数据缩减的高效且精确的方法

基本信息

批准号：
1908198
负责人：
Shuiwang Ji
金额：
$ 39.68万
依托单位：
Texas A&M Engineering Experiment Station
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-16 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908198&HistoricalAwards=false
关键词：
BIGDATA Collaborative Research Efficient Exact

项目摘要

AbstractResearch in big data involves analyzing growing data sets with huge numbers of samples, very high-dimensional feature vectors, and complex and diverse structures. The ever-growing volume and complexity of these data sets make many traditional techniques inadequate to extract knowledge from them. An emerging area, known as sparse learning, has achieved great success in learning from big data by identifying a small set of explanatory features and/or samples. Typical examples include selecting features that are most indicative of users? preferences for recommendation systems, identifying brain regions that are predictive of neurological disorders based on imaging data, and extracting semantic information from raw images for object recognition. However, training sparse learning models can be computationally prohibitive due to the sparsity-inducing regularization, which is non-smooth and can be highly complex when incorporating complex structures. This project aims at developing algorithms and tools to significantly accelerate the training process of sparse learning models for big data applications. The key idea is to efficiently identify redundant features and/or samples, which can be removed from the training phase without losing useful information of interests. Success in these unique techniques is expected to dramatically scaling up sparse learning for big data by orders of magnitude in terms of both time and space. The PIs plan to integrate the big data reduction tools developed in this project into their education and outreach activities, including development of new courses and integration of project components into existing courses. The PIs will make special efforts to recruit female and underrepresented students to this project.The major technical innovations of this project include the following components: (1) the PIs will develop efficient feature reduction methods for the generic scenario where the structures of both input and output can be represented by directed acyclic graphs; the proposed formulations include many existing approaches as special cases; (2) the PIs will develop efficient methods to reduce the numbers of features and samples simultaneously under a unified formulation, which can also incorporate various structures; (3) the PIs will develop efficient methods to discard irrelevant data subspaces to accelerate the process of uncovering low-rank structures commonly seen in big data. All the proposed data reduction methods are exact, i.e., the models learned on the reduced data sets are identical to the ones learned on the full data sets. This project heavily relies on optimization theory, especially on sensitivity analysis and convex geometry. The outcome of this project includes a unified approach to accelerate sparse learning and provide a systematic framework for developing efficient and exact data reduction methods. The systematic study and in-depth exploration of redundant data identification is expected to deepen the understanding of sparse learning techniques and dramatically enhance their applications in big data analytics.

大数据中的Abstractresearch涉及分析具有大量样品，非常高维的矢量以及复杂和多样化结构的增长数据集。这些数据集的不断增长和复杂性使许多传统技术不足以从中提取知识。一个被称为稀疏学习的新兴领域通过识别一小部分解释性特征和/或样本，在从大数据中学习取得了巨大的成功。典型的示例包括选择最指示用户的功能？推荐系统的偏好，确定基于成像数据的神经系统疾病的大脑区域，并从原始图像中提取语义信息以识别对象识别。但是，由于引起稀疏性的正则化，训练稀疏学习模型在计算上可能是过度的，这是非平滑的，并且在融合复杂的结构时可能非常复杂。该项目旨在开发算法和工具，以显着加速大数据应用程序稀疏学习模型的培训过程。关键思想是有效地识别冗余特征和/或样本，可以从训练阶段删除，而不会失去利益的有用信息。在这些独特的技术中的成功有望通过时间和空间来大大扩展大数据的稀疏学习。 PIS计划将该项目中开发的大数据减少工具整合到他们的教育和外展活动中，包括开发新课程以及将项目组件的整合到现有课程中。 PI将特别努力招募女性和代表性不足的学生参加该项目。该项目的主要技术创新包括以下组成部分：（1）PIS将开发出有效的降低功能方法，以便为通用场景提供了投入和输入结构的一般情况。输出可以用定向的无环图表示；拟议的配方包括许多现有方法作为特殊情况；（2）PIS将开发有效的方法，以减少统一配方的特征和样品的数量，这也可以结合各种结构；（3）PIS将开发有效的方法来丢弃无关的数据子空间，以加速揭示大数据中常见的低级别结构的过程。所有提出的数据还原方法都是精确的，即，在还原数据集中学习的模型与在完整数据集中学到的模型相同。该项目在很大程度上取决于优化理论，尤其是灵敏度分析和凸几何。该项目的结果包括一种统一的方法来加速稀疏学习，并为开发有效而精确的数据减少方法提供了系统的框架。预期对冗余数据识别的系统研究和深入探索，可以加深对稀疏学习技术的理解，并大大增强其在大数据分析中的应用。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Towards Deeper Graph Neural Networks

DOI：
10.1145/3394486.3403076
发表时间：
2020-01-01
期刊：
KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
影响因子：
0
作者：
Liu, Meng;Gao, Hongyang;Ji, Shuiwang
通讯作者：
Ji, Shuiwang

GraphFM: Improving Large-Scale GNN Training via Feature Momentum