III: Small: Unsupervised Feature Selection in the Era of Big Data

特征选择已被证明是高效和有效的准备高维数据的数据挖掘和机器学习应用程序，特别是当原始特征是重要的模型解释和知识提取。随着数据收集能力的急剧增加，数据的规模和复杂性都在迅速增长。这些大数据对传统的特征选择方法提出了巨大的挑战，这些方法通常被设计为以集中的方式处理同质和静态数据。与此同时，在许多现实世界的领域，大数据是未标记的，这进一步加剧了困难。因此，大多数现有的特征选择方法都没有为大数据做好充分的准备，这就需要为未标记的大数据开发新的无监督特征选择。该项目将最先进的特征选择研究扩展到驯服大数据的新前沿。它有可能使计算机科学，商业，教育，政治，医疗保健和生物信息学等各个学科的许多现实世界的应用程序受益。该项目提出了一套用于无监督特征选择的新方法，以促进对大数据的计算理解，调查相关的基础研究问题并开发有效的算法。它包括三个主要的推力。 首先，它研究了各种策略来扩展无监督特征选择以处理大规模和分布式数据;并研究了具有结构化特征和异步更新的分布式无监督特征选择。其次，它开发了一个家庭的异构无监督特征选择与多种类型的异质性。 第三，它定义了各种流媒体场景的无监督特征选择，并开发了新的算法来提高无监督特征选择在处理相应流媒体设置时的能力。计划采用不同的方式传播该项目及其研究结果，包括网络数据和软件储存库、书籍、期刊和会议出版物、特殊目的讲习班或教程以及外部合作。该项目融合了特征选择、大数据分析、机器学习和数据挖掘。它可以有效地整合到本科和研究生课程，以及在学生的研究项目。

Feature selection has been proven to be efficient and effective in preparing high-dimensional data for data mining and machine learning applications, especially when the original features are important for model interpretation and knowledge extraction. The growth of data in both size and complexity accelerates rapidly as the dramatic increase of the capacity to collect data. Such big data has imposed tremendous challenges on traditional feature selection methods, which are usually designed to handle homogeneous and static data in a centralized fashion. Meanwhile in many real-world domains big data is unlabeled, which further exacerbates the difficulty. Therefore, the majority of existing feature selection methods are not well prepared for big data, and this thus calls for the development of novel unsupervised feature selection for unlabeled big data. The project extends the state-of-the-art feature selection research to a new frontier of taming big data. It has potential to benefit a number of real-world applications from various disciplines such as Computer Science, Business, Education, Politics, Healthcare and Bioinformatics. This project proposes a suite of novel approaches for unsupervised feature selection to facilitate the computational understanding of big data, investigating associated fundamental research issues and developing effective algorithms. It consists of three major thrusts. First, it studies various strategies to scale unsupervised feature selection to handle large-scale and distributed data; and investigates distributed unsupervised feature selection with structured features and under asynchronous updates. Second, it develops a family of heterogeneous unsupervised feature selection with multiple types of heterogeneity. Third, it defines the unsupervised feature selection with various streaming scenarios, and develops new algorithms to improve the capability of unsupervised feature selection in handling the corresponding streaming settings. Disparate means are planned to disseminate the project and its findings, including web enabled data and software repositories, books, journal and conference publications, special-purpose workshops or tutorials, and external collaborations. The project lies at the confluence of feature selection, big data analysis, machine learning and data mining. It can be effectively integrated to undergraduate and graduate courses as well as in student research projects.