BIGDATA: F: DKA: CSD: Topological Data Analysis and Machine-Learning with Community-Accepted Features

Duke University, Durham NC

Investigators

Abstract

This project develops a new set of techniques from topological data analysis (TDA) to enrich the analytical toolkit for big data problems. In particular, TDA is well-suited at picking up repetitive (even quasi-repetitive) patterns that exist at many different scale levels within a dataset. The developed techniques can be applied to many multimedia applications. For example, a system that can correctly recognize certain motion patterns from a large set of surveillance data can help to identify threatening situations as they arise. Or a methodology that takes short song snippets and extract indicators of genre similarity may eventually be used to suggest new sound patterns. This project constructs an analytical pipeline for using TDA-ML (machine-learning) methods on features already in use in the multimedia research communities. Topological Data Analysis (TDA) is almost fifteen years old. One of its key tools is the persistence diagram (PD), a compact and robust summary of the low-dimensional multi-scale topological and geometric information in a high-dimensional point cloud. Crucially, this information is extracted without need for dimension reduction. Over the last few years, two exciting developments have enriched TDA. First, theoretical and practical work on algorithms and implementations has enabled the fast computation of large numbers of PDs. Second, a coherent methodology has developed to do machine-learning (ML) with PDs as features, with several examples showing that one can augment more-standard feature sets with PD features, and find interesting signal that was not apparent before. This research discovers compelling signals in these features which are not previously apparent, but are immediately understandable because of the choice of features. The research team investigates both video and audio features. The ML methods are used to further measure importance of the features in a given context.