• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to project page

2012 Fiscal Year Final Research Report

Machine learning based on sparsity-inducing regularization for matrices

Research Project

  • PDF
Project/Area Number 22700138
Research Category

Grant-in-Aid for Young Scientists (B)

Allocation TypeSingle-year Grants
Research Field Intelligent informatics
Research InstitutionThe University of Tokyo

Principal Investigator

TOMIOKA Ryota  東京大学, 大学院・情報理工学系研究科, 助教 (70518282)

Project Period (FY) 2010 – 2012
Keywords機械学習 / スパース性 / 正則化 / 行列 / テンソル
Research Abstract

The outcomes of this research project can be summarized as follows:1. I have extended the dual augmented Lagrangian (DAL) algorithm to deal with spectral regularization for matrices and proposed the M-DAL algorithm(ICML2010). The super-linear convergence of DAL and M-DAL algorithms wasproven and published in JMLR. A review of DAL and related algorithms has beenpublished as part of “Optimization for Machine Learning” (MIT Press). I have alsomade the code publicly available to promote its use in wider research communities. 2. In order to deal with non-numerical data, I have extended DAL to handle multiplekernel learning with thousands of kernels. This was published in MachineLearning Journal. 3. I have extended the framework to spectral regularization for higher-order tensors and analyzed its statistical performance. This was presented at NIPS2011.

  • Research Products

    (13 results)

All 2013 2012 2011 2010 Other

All Journal Article (4 results) Presentation (6 results) Book (1 results) Remarks (2 results)

  • [Journal Article] Global analytic solution of fully-observed variational Bayesian matrix factorization.2013

    • Author(s)
      S. Nakajima, M. Sugiyama, S. Babacan,and R. Tomioka.
    • Journal Title

      J. Mach.Learn. Res.

      Volume: 14 Pages: 1-37

  • [Journal Article] Discovering emerging topics in social streams via link anomaly detection.2012

    • Author(s)
      T. Takahashi, R. Tomioka, and K. Yamanishi
    • Journal Title

      IEEE Transactions on Knowledge and Data Engineering.

      Volume: (Accepted.)

  • [Journal Article] Super-linear convergence of dual augmented Lagrangian algorithm for sparse learning.2011

    • Author(s)
      R. Tomioka, T. Suzuki, and M. Sugiyama
    • Journal Title

      Journal of Machine Learning Research

      Volume: 12 Pages: 1537-1586.

  • [Journal Article] SpicyMKL: A Fast Algorithm for Multiple Kernel Learning with Thousands of Kernels.2011

    • Author(s)
      T. Suzuki and R. Tomioka
    • Journal Title

      Machine Learning

      Volume: 85 Pages: 77-108.

  • [Presentation] A Bayesian Analysis of the Radioactive Releases of Fukushima.2012

    • Author(s)
      R. Tomioka and M. Morup
    • Organizer
      In JMLUR Workshop and Conference Proceedings 22 (AISTATS2012)
    • Year and Date
      20120000
  • [Presentation] A combinatorial algebraic approach for the identifiability of low-rank matrix completion.2012

    • Author(s)
      F. Kiraly and R. Tomioka
    • Organizer
      In Proc. 29th International Conference on Machine Learning
    • Year and Date
      20120000
  • [Presentation] Perfect dimensionality recovery by variational Bayesian PCA.2012

    • Author(s)
      S. Nakajima, R. Tomioka, M. Sugiyama,and S. Babacan
    • Organizer
      In Advances in Neural Information Processing Systems
    • Year and Date
      20120000
  • [Presentation] Discovering emerging topics in social streams via link anomaly detection.2011

    • Author(s)
      T. Takahashi, R. Tomioka, and K.Yamanishi.
    • Organizer
      In Proc. of the 11th International Conference on Data Mining(ICDM2011)
    • Year and Date
      20110000
  • [Presentation] Statistical Performance of Convex Tensor Decomposition.2011

    • Author(s)
      R Tomioka, T. Suzuki, K. Hayashi, and H. Kashima
    • Organizer
      In Advances in Neural Information Processing Systems
    • Year and Date
      20110000
  • [Presentation] A fast augmented Lagrangian algorithm for learning low-rank matrices.2010

    • Author(s)
      R. Tomioka, T. Suzuki, M. Sugiyama, and H. Kashima.
    • Organizer
      In Proc. the 27th Annual International Conference on Machine Learning (ICML2010)
    • Place of Presentation
      Omnipress
    • Year and Date
      20100000
  • [Book] Augmented Lagrangian methods for learning, selecting, and combining features.2011

    • Author(s)
      R. Tomioka, T. Suzuki, and M. Sugiyama.
    • Publisher
      MIT Press.
  • [Remarks]

    • URL

      http://www.ibis.t.u-tokyo.ac.jp/ryotat/

  • [Remarks] 双対拡張ラグランジュ法

    • URL

      http://www.ibis.t.u-tokyo.ac.jp/ryotat/dal/

URL: 

Published: 2014-08-29  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi