Efficient optimization for linear dynamical systems with applications to clustering and sparse coding

Wenbing Huang, Mehrtash Harandi, Tong Zhang, Lijie Fan, Fuchun Sun, Junzhou Huang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)

Abstract

Linear Dynamical Systems (LDSs) are fundamental tools for modeling spatiotemporal data in various disciplines. Though rich in modeling, analyzing LDSs is not free of difficulty, mainly because LDSs do not comply with Euclidean geometry and hence conventional learning techniques can not be applied directly. In this paper, we propose an efficient projected gradient descent method to minimize a general form of a loss function and demonstrate how clustering and sparse coding with LDSs can be solved by the proposed method efficiently. To this end, we first derive a novel canonical form for representing the parameters of an LDS, and then show how gradient-descent updates through the projection on the space of LDSs can be achieved dexterously. In contrast to previous studies, our solution avoids any approximation in LDS modeling or during the optimization process. Extensive experiments reveal the superior performance of the proposed method in terms of the convergence and classification accuracy over state-of-the-art techniques.

Original languageEnglish
Title of host publicationNIPS Proceedings
Subtitle of host publicationAdvances in Neural Information Processing Systems 30 (NIPS 2017)
EditorsI. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett
Place of PublicationLa Jolla CA USA
PublisherNeural Information Processing Systems (NIPS)
Pages3445-3455
Number of pages11
Publication statusPublished - 1 Jan 2017
Externally publishedYes
EventAdvances in Neural Information Processing Systems 2017 - Long Beach, United States of America
Duration: 4 Dec 20179 Dec 2017
Conference number: 30th
https://dl.acm.org/doi/proceedings/10.5555/3295222 (Proceedings)

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

ConferenceAdvances in Neural Information Processing Systems 2017
Abbreviated titleNIPS 2017
CountryUnited States of America
CityLong Beach
Period4/12/179/12/17
Internet address

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