10,304 research outputs found
A Beta-mixture model for dimensionality reduction, sample classification and analysis
<p>Abstract</p> <p>Background</p> <p>Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper we propose a beta-mixture model to describe genome-wide methylation patterns based on probe data from methylation microarrays. The model takes dependencies between neighbour probe pairs into account and assumes three broad categories of methylation, low, medium and high. The model is described by 37 parameters, which reduces the dimensionality of a typical methylation microarray significantly. We used methylation microarray data from 42 colon cancer samples to assess the model.</p> <p>Results</p> <p>Based on data from colon cancer samples we show that our model captures genome-wide characteristics of methylation patterns. We estimate the parameters of the model and show that they vary between different tissue types. Further, for each methylation probe the posterior probability of a methylation state (low, medium or high) is calculated and the probability that the state is correctly predicted is assessed. We demonstrate that the model can be applied to classify cancer tissue types accurately and that the model provides accessible and easily interpretable data summaries.</p> <p>Conclusions</p> <p>We have developed a beta-mixture model for methylation microarray data. The model substantially reduces the dimensionality of the data. It can be used for further analysis, such as sample classification or to detect changes in methylation status between different samples and tissues.</p
Nonparametric Weight Initialization of Neural Networks via Integral Representation
A new initialization method for hidden parameters in a neural network is
proposed. Derived from the integral representation of the neural network, a
nonparametric probability distribution of hidden parameters is introduced. In
this proposal, hidden parameters are initialized by samples drawn from this
distribution, and output parameters are fitted by ordinary linear regression.
Numerical experiments show that backpropagation with proposed initialization
converges faster than uniformly random initialization. Also it is shown that
the proposed method achieves enough accuracy by itself without backpropagation
in some cases.Comment: For ICLR2014, revised into 9 pages; revised into 12 pages (with
supplements
Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams
The name entity disambiguation task aims to partition the records of multiple
real-life persons so that each partition contains records pertaining to a
unique person. Most of the existing solutions for this task operate in a batch
mode, where all records to be disambiguated are initially available to the
algorithm. However, more realistic settings require that the name
disambiguation task be performed in an online fashion, in addition to, being
able to identify records of new ambiguous entities having no preexisting
records. In this work, we propose a Bayesian non-exhaustive classification
framework for solving online name disambiguation task. Our proposed method uses
a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model
which enables identification of new ambiguous entities who have no records in
the training data. For online classification, we use one sweep Gibbs sampler
which is very efficient and effective. As a case study we consider
bibliographic data in a temporal stream format and disambiguate authors by
partitioning their papers into homogeneous groups. Our experimental results
demonstrate that the proposed method is better than existing methods for
performing online name disambiguation task.Comment: to appear in CIKM 201
A nonparametric Bayesian approach toward robot learning by demonstration
In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose. A major limitation of GMR models concerns automatic selection of the proper number of model states, i.e., the number of model component densities. Existing methods, including likelihood- or entropy-based criteria, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally investigate its efficacy as a robot learning by demonstration methodology, considering a number of demanding robot learning by demonstration scenarios
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