Skip to main content
placeholder image

PSDBoost: Matrix-generation linear programming for positive semidefinite matrices learning

Conference Paper


Download full-text (Open Access)

Abstract


  • In this work, we consider the problem of learning a positive semidefinite matrix.

    The critical issue is how to preserve positive semidefiniteness during the course

    of learning. Our algorithm is mainly inspired by LPBoost [1] and the general

    greedy convex optimization framework of Zhang [2]. We demonstrate the essence

    of the algorithm, termed PSDBoost (positive semidefinite Boosting), by focusing

    on a few different applications in machine learning. The proposed PSDBoost

    algorithm extends traditional Boosting algorithms in that its parameter is a positive

    semidefinite matrix with trace being one instead of a classifier. PSDBoost is

    based on the observation that any trace-one positive semidefinite matrix can be decomposed

    into linear convex combinations of trace-one rank-one matrices, which

    serve as base learners of PSDBoost. Numerical experiments are presented.

Authors


  •   Shen, Chunhua (external author)
  •   Welsh, Alan (external author)
  •   Wang, Lei

Publication Date


  • 2008

Citation


  • Shen, C., Welsh, A. & Wang, L. (2008). PSDBoost: Matrix-generation linear programming for positive semidefinite matrices learning. Proceedings of Advances in Neural Information Processing Systems (NIPS) (pp. 1-8).

Scopus Eid


  • 2-s2.0-84863362632

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1652&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/646

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 8

Abstract


  • In this work, we consider the problem of learning a positive semidefinite matrix.

    The critical issue is how to preserve positive semidefiniteness during the course

    of learning. Our algorithm is mainly inspired by LPBoost [1] and the general

    greedy convex optimization framework of Zhang [2]. We demonstrate the essence

    of the algorithm, termed PSDBoost (positive semidefinite Boosting), by focusing

    on a few different applications in machine learning. The proposed PSDBoost

    algorithm extends traditional Boosting algorithms in that its parameter is a positive

    semidefinite matrix with trace being one instead of a classifier. PSDBoost is

    based on the observation that any trace-one positive semidefinite matrix can be decomposed

    into linear convex combinations of trace-one rank-one matrices, which

    serve as base learners of PSDBoost. Numerical experiments are presented.

Authors


  •   Shen, Chunhua (external author)
  •   Welsh, Alan (external author)
  •   Wang, Lei

Publication Date


  • 2008

Citation


  • Shen, C., Welsh, A. & Wang, L. (2008). PSDBoost: Matrix-generation linear programming for positive semidefinite matrices learning. Proceedings of Advances in Neural Information Processing Systems (NIPS) (pp. 1-8).

Scopus Eid


  • 2-s2.0-84863362632

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1652&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/646

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 8