13,731 research outputs found
Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures
Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data
Hierarchically Clustered Representation Learning
The joint optimization of representation learning and clustering in the
embedding space has experienced a breakthrough in recent years. In spite of the
advance, clustering with representation learning has been limited to flat-level
categories, which often involves cohesive clustering with a focus on instance
relations. To overcome the limitations of flat clustering, we introduce
hierarchically-clustered representation learning (HCRL), which simultaneously
optimizes representation learning and hierarchical clustering in the embedding
space. Compared with a few prior works, HCRL firstly attempts to consider a
generation of deep embeddings from every component of the hierarchy, not just
leaf components. In addition to obtaining hierarchically clustered embeddings,
we can reconstruct data by the various abstraction levels, infer the intrinsic
hierarchical structure, and learn the level-proportion features. We conducted
evaluations with image and text domains, and our quantitative analyses showed
competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape
Improved Bayesian Compression
Compression of Neural Networks (NN) has become a highly studied topic in
recent years. The main reason for this is the demand for industrial scale usage
of NNs such as deploying them on mobile devices, storing them efficiently,
transmitting them via band-limited channels and most importantly doing
inference at scale. In this work, we propose to join the Soft-Weight Sharing
and Variational Dropout approaches that show strong results to define a new
state-of-the-art in terms of model compression
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
A general framework for solving image inverse problems is introduced in this
paper. The approach is based on Gaussian mixture models, estimated via a
computationally efficient MAP-EM algorithm. A dual mathematical interpretation
of the proposed framework with structured sparse estimation is described, which
shows that the resulting piecewise linear estimate stabilizes the estimation
when compared to traditional sparse inverse problem techniques. This
interpretation also suggests an effective dictionary motivated initialization
for the MAP-EM algorithm. We demonstrate that in a number of image inverse
problems, including inpainting, zooming, and deblurring, the same algorithm
produces either equal, often significantly better, or very small margin worse
results than the best published ones, at a lower computational cost.Comment: 30 page
Bayesian Agglomerative Clustering with Coalescents
We introduce a new Bayesian model for hierarchical clustering based on a
prior over trees called Kingman's coalescent. We develop novel greedy and
sequential Monte Carlo inferences which operate in a bottom-up agglomerative
fashion. We show experimentally the superiority of our algorithms over others,
and demonstrate our approach in document clustering and phylolinguistics.Comment: NIPS 200
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