28,654 research outputs found
Bayesian learning of joint distributions of objects
There is increasing interest in broad application areas in defining flexible
joint models for data having a variety of measurement scales, while also
allowing data of complex types, such as functions, images and documents. We
consider a general framework for nonparametric Bayes joint modeling through
mixture models that incorporate dependence across data types through a joint
mixing measure. The mixing measure is assigned a novel infinite tensor
factorization (ITF) prior that allows flexible dependence in cluster allocation
across data types. The ITF prior is formulated as a tensor product of
stick-breaking processes. Focusing on a convenient special case corresponding
to a Parafac factorization, we provide basic theory justifying the flexibility
of the proposed prior and resulting asymptotic properties. Focusing on ITF
mixtures of product kernels, we develop a new Gibbs sampling algorithm for
routine implementation relying on slice sampling. The methods are compared with
alternative joint mixture models based on Dirichlet processes and related
approaches through simulations and real data applications.Comment: Appearing in Proceedings of the 16th International Conference on
Artificial Intelligence and Statistics (AISTATS) 2013, Scottsdale, AZ, US
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis
The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability to weight the base clusterings without access to the original data and
can be affected significantly by the low-quality, or even ill clusterings.
Secondly, they generally focus on the instance level or cluster level in the
ensemble system and fail to integrate multi-granularity cues into a unified
model. To address these two limitations, this paper proposes to solve the
clustering ensemble problem via crowd agreement estimation and
multi-granularity link analysis. We present the normalized crowd agreement
index (NCAI) to evaluate the quality of base clusterings in an unsupervised
manner and thus weight the base clusterings in accordance with their clustering
validity. To explore the relationship between clusters, the source aware
connected triple (SACT) similarity is introduced with regard to their common
neighbors and the source reliability. Based on NCAI and multi-granularity
information collected among base clusterings, clusters, and data instances, we
further propose two novel consensus functions, termed weighted evidence
accumulation clustering (WEAC) and graph partitioning with multi-granularity
link analysis (GP-MGLA) respectively. The experiments are conducted on eight
real-world datasets. The experimental results demonstrate the effectiveness and
robustness of the proposed methods.Comment: The MATLAB source code of this work is available at:
https://www.researchgate.net/publication/28197031
Ensemble clustering for result diversification
This paper describes the participation of the University of Twente in the Web track of TREC 2012. Our baseline approach uses the Mirex toolkit, an open source tool that sequantially scans all the documents. For result diversification, we experimented with improving the quality of clusters through ensemble clustering. We combined clusters obtained by different clustering methods (such as LDA and K-means) and clusters obtained by using different types of data (such as document text and anchor text). Our two-layer ensemble run performed better than the LDA based diversification and also better than a non-diversification run
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