370 research outputs found
Algorithms for item categorization based on ordinal ranking data
We present a new method for identifying the latent categorization of items
based on their rankings. Complimenting a recent work that uses a Dirichlet
prior on preference vectors and variational inference, we show that this
problem can be effectively dealt with using existing community detection
algorithms, with the communities corresponding to item categories. In
particular we convert the bipartite ranking data to a unipartite graph of item
affinities, and apply community detection algorithms. In this context we modify
an existing algorithm - namely the label propagation algorithm to a variant
that uses the distance between the nodes for weighting the label propagation -
to identify the categories. We propose and analyze a synthetic ordinal ranking
model and show its relation to the recently much studied stochastic block
model. We test our algorithms on synthetic data and compare performance with
several popular community detection algorithms. We also test the method on real
data sets of movie categorization from the Movie Lens database. In all of the
cases our algorithm is able to identify the categories for a suitable choice of
tuning parameter.Comment: To appear in IEEE Allerton conference on computing, communications
and control, 201
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify
clusters based on their associations with subspaces in high dimensional spaces.
SC can be classified into hard subspace clustering (HSC) and soft subspace
clustering (SSC). While HSC algorithms have been extensively studied and well
accepted by the scientific community, SSC algorithms are relatively new but
gaining more attention in recent years due to better adaptability. In the
paper, a comprehensive survey on existing SSC algorithms and the recent
development are presented. The SSC algorithms are classified systematically
into three main categories, namely, conventional SSC (CSSC), independent SSC
(ISSC) and extended SSC (XSSC). The characteristics of these algorithms are
highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201
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