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Masashi Furukawa

  1. On the clustering aspect of nonnegative matrix factorization.

    Втр, 06/01/2010 - 15:02 — SomNambul
    Authors: Andri Mirzal, Masashi Furukawa
    Subjects: Learning
    Abstract

    This paper provides a theoretical explanation on the clustering aspect of
    nonnegative matrix factorization (NMF). We prove that even without imposing
    orthogonality or sparsity constraint on the basis and/or coefficient matrix,
    NMF still can give clustering results, thus providing a theoretical support for
    the works of Xu et al. [1] and Kim et al. [2], where the authors showed the
    superiority of the standard NMF as a clustering method.

  2. Node-Context Network Clustering using PARAFAC Tensor Decomposition.

    Втр, 05/04/2010 - 15:01 — SomNambul
    Authors: Andri Mirzal, Masashi Furukawa
    Subjects: Information Retrieval
    Abstract

    We describe a clustering method for labeled link network (semantic graph)
    that can be used to group important nodes (highly connected nodes) with their
    relevant link's labels by using PARAFAC tensor decomposition. In this kind of
    network, the adjacency matrix can not be used to fully describe all information
    about the network structure. We have to expand the matrix into 3-way adjacency
    tensor, so that not only the information about to which nodes a node connects
    to but by which link's labels is also included.

  3. A Method for Accelerating the HITS Algorithm.

    Пт, 09/04/2009 - 12:10 — SomNambul
    Authors: Andri Mirzal, Masashi Furukawa
    Subjects: Information Retrieval
    Abstract

    We present a new method to accelerate the HITS algorithm by exploiting
    hyperlink structure of the web graph. The proposed algorithm extends the idea
    of authority and hub scores from HITS by introducing two diagonal matrices
    which contain constants that act as weights to make authority pages more
    authoritative and hub pages more hubby. This method works because in the web
    graph good authorities are pointed to by good hubs and good hubs point to good
    authorities. Consequently, these pages will collect their scores faster under
    the proposed algorithm than under the standard HITS.

Math-arch

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