Representation learning on sequential and temporal data manifolds

Maryam Daniali

doi:10.17918/00001637

Representation learning has gained significant attention in the machine learning community due to its ability to automatically extract useful features from raw data without the need for hand-crafted feature engineering. Although representation learning is widely applied to image data, it has received less attention in the context of sequential and temporal data manifolds, despite the growing prevalence of such data in industry and on the internet. This dissertation focuses on utilizing representation learning in applications with sequential and temporal data and demonstrates its advantages over conventional techniques that do not incorporate a sequential component. We investigate the different tools and techniques used in representation learning for sequential and temporal data, compare their strengths and weaknesses, and provide guidelines for implementing them in various domains. The study also presents a number of case studies in a variety of domains to demonstrate the benefits of representation learning. In the time series case study, representation learning is applied to analyze the driving behavior of individuals with cognitive impairments, demonstrating its effectiveness over traditional techniques. In the text and graph case study, representation learning is extended to clinical graphs to present disease information in an interpretable space, measuring patient similarity based on their visit notes. In the image case study, transfer learning is used to improve object classification models via neuro-inspired approaches to enhance representation learning for static image classification. This case study explores the creation of a dynamic perception framework that is robust to minor perturbations in input stimuli through representation learning over time. Among the contributions of this dissertation are a comprehensive review of representation learning techniques for sequential and temporal data manifolds. Also included are guidelines for their use in various domains with such data, as well as real-world case studies demonstrating their effectiveness. The purpose of this dissertation is to provide insight into how representation learning can improve models' performance, reduce data dimensionality, and facilitate models' interpretation.

Representation learning on sequential and temporal data manifolds

Files and links (1)

Abstract

Metrics

Details

Representation learning on sequential and temporal data manifolds

Files and links (1)

Abstract

Metrics

Details

Drexel University Social media