Signal Processing and Information Extraction, from Data to Complex Models and Structures

信号处理和信息提取，从数据到复杂模型和结构

• 批准号: RGPIN-2018-04079
• 负责人: beheshti, soosan
• 金额: $ 2.4万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=644988
• 关键词: Signal; Processing; Information; Extraction; Data

This research focuses on statistical learning of complex systems and structures in presence of large scale data. Our increasing ability to collect massive volumes of data demands an urgent need for analysis, interpretation, and modeling of the underlying structures of these collected data which is the main motivation of this work. Trustworthy data modeling becomes vital especially in scenarios where the use of unreliable model can cause irreversible damages. Examples of such cases are actions based on various health sector data analysis and environmental decisions based on conclusions from large collected data. Despite advantages of our ability in storing the “big” data, they also come with inherent “big” challenges for proper information extraction. Huge data set size, in both cardinality and member dimension, requires extracting lower dimension informative data in real applications. Much of research in this area is now dedicated to data sketching through approaches such as development of efficient tensor analysis and/or design of adaptive kernel functions. These data modeling methods are heavily sensitive to the inevitable dimension reduction process. The recent modeling approaches for large scale data take advantages of including latent variables in the parametric modeling. For example, one popular approach in this direction is deep learning. While these methods are quite successful with noise removal on the large data sets, issues such as uncertainty in labeling, overfitting, and verification of convergence to the true model are extremely important in these approaches. In addition, in all the research trends for large data modeling, simultaneous complexity analysis and robust performance are critical tasks in various machine learning methods such as data clustering, classification, compression, and predictive structural modeling. The main concentration of this project is on these important challenges of large data modeling. It will focus on validation approaches for structural data modeling, complexity analysis, and optimum choice of number of parameters in modeling. As more data becomes available, the modeling methods have the luxury of becoming more complex with higher number of parameters. The crucial challenge, however, is that the rate of growth in modeling complexity has to be monitored such that it can guarantee providing more precise modeling and doesn't lose reliability by overfitting. The intended outcome of this research provides theory, knowledge, and algorithms for feasible data-driven structural modeling. Immediate impact of the project is in developing efficient, reliable, and most importantly verifiable methods of structural data modeling. *****

本研究的重点是在大数据环境下复杂系统和结构的统计学习。我们收集大量数据的能力日益增强，迫切需要对这些收集到的数据的底层结构进行分析、解释和建模，这是这项工作的主要动机。值得信赖的数据建模变得至关重要，特别是在使用不可靠模型可能造成不可逆转损害的场景中。这方面的例子包括根据各种卫生部门数据分析采取的行动，以及根据收集到的大量数据得出的结论作出的环境决定。尽管我们在存储“大”数据方面有优势，但它们也带来了固有的“大”挑战，即如何正确提取信息。在实际应用中，庞大的数据集在基数和成员维度上都需要提取较低维度的信息数据。该领域的许多研究现在都致力于通过开发高效张量分析和/或设计自适应核函数等方法来绘制数据草图。这些数据建模方法对不可避免的降维过程非常敏感。最近的大规模数据建模方法利用了在参数化建模中包含潜在变量的优点。例如，在这个方向上一个流行的方法是深度学习。虽然这些方法在去除大型数据集上的噪声方面非常成功，但在这些方法中，诸如标记的不确定性、过拟合和对真实模型的收敛性验证等问题非常重要。此外，在大数据建模的所有研究趋势中，同时进行复杂性分析和鲁棒性性能是各种机器学习方法（如数据聚类、分类、压缩和预测结构建模）的关键任务。这个项目主要集中在大数据建模的这些重要挑战上。它将侧重于结构数据建模的验证方法，复杂性分析，以及建模中参数数量的最佳选择。随着可用的数据越来越多，建模方法变得越来越复杂，参数数量也越来越多。然而，关键的挑战是必须监控建模复杂性的增长率，这样才能保证提供更精确的建模，并且不会因过拟合而失去可靠性。本研究的预期结果为可行的数据驱动结构建模提供了理论、知识和算法。项目的直接影响是开发高效、可靠、最重要的是可验证的结构数据建模方法。＊＊＊＊＊