상세정보
Export to Refworks부가기능
Stochastic Processes on Graphs: Learning Representations and Applications
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Stochastic Processes on Graphs: Learning Representations and Applications. |
| 개인저자 | Bohannon, Addison W. |
| 단체저자명 | University of Maryland, College Park. Applied Mathematics and Scientific Computation. |
| 발행사항 | [S.l.]: University of Maryland, College Park., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 157 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085614276 |
| 학위논문주기 | Thesis (Ph.D.)--University of Maryland, College Park, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Balan, Radu V. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | In this work, we are motivated by discriminating multivariate time-series with an underlying graph topology. Graph signal processing has developed various tools for the analysis of scalar signals on graphs. Here, we extend the existing techniques to design filters for multivariate time-series that have non-trivial spatiotemporal graph topologies. We show that such a filtering approach can discriminate signals that cannot otherwise be discriminated by competing approaches. Then, we consider how to identify spatiotemporal graph topology from signal observations. Specifically, we consider a generative model that yields a bilinear inverse problem with an observation-dependent left multiplication. We propose two algorithms for solving the inverse problem and provide probabilistic guarantees on recovery. We apply the technique to identify spatiotemporal graph components in electroencephalogram (EEG) recordings. The identified components are shown to discriminate between various cognitive task conditions in the data. |
| 일반주제명 | Applied mathematics. Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
