22 research outputs found
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This paper presents a methodology for creating streaming, distributed
inference algorithms for Bayesian nonparametric (BNP) models. In the proposed
framework, processing nodes receive a sequence of data minibatches, compute a
variational posterior for each, and make asynchronous streaming updates to a
central model. In contrast to previous algorithms, the proposed framework is
truly streaming, distributed, asynchronous, learning-rate-free, and
truncation-free. The key challenge in developing the framework, arising from
the fact that BNP models do not impose an inherent ordering on their
components, is finding the correspondence between minibatch and central BNP
posterior components before performing each update. To address this, the paper
develops a combinatorial optimization problem over component correspondences,
and provides an efficient solution technique. The paper concludes with an
application of the methodology to the DP mixture model, with experimental
results demonstrating its practical scalability and performance.Comment: This paper was presented at NIPS 2015. Please use the following
BibTeX citation: @inproceedings{Campbell15_NIPS, Author = {Trevor Campbell
and Julian Straub and John W. {Fisher III} and Jonathan P. How}, Title =
{Streaming, Distributed Variational Inference for Bayesian Nonparametrics},
Booktitle = {Advances in Neural Information Processing Systems (NIPS)}, Year
= {2015}
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
This paper presents a novel algorithm, based upon the dependent Dirichlet
process mixture model (DDPMM), for clustering batch-sequential data containing
an unknown number of evolving clusters. The algorithm is derived via a
low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM,
and provides a hard clustering with convergence guarantees similar to those of
the k-means algorithm. Empirical results from a synthetic test with moving
Gaussian clusters and a test with real ADS-B aircraft trajectory data
demonstrate that the algorithm requires orders of magnitude less computational
time than contemporary probabilistic and hard clustering algorithms, while
providing higher accuracy on the examined datasets.Comment: This paper is from NIPS 2013. Please use the following BibTeX
citation: @inproceedings{Campbell13_NIPS, Author = {Trevor Campbell and Miao
Liu and Brian Kulis and Jonathan P. How and Lawrence Carin}, Title = {Dynamic
Clustering via Asymptotics of the Dependent Dirichlet Process}, Booktitle =
{Advances in Neural Information Processing Systems (NIPS)}, Year = {2013}
Bayesian Nonparametric Unmixing of Hyperspectral Images
Hyperspectral imaging is an important tool in remote sensing, allowing for
accurate analysis of vast areas. Due to a low spatial resolution, a pixel of a
hyperspectral image rarely represents a single material, but rather a mixture
of different spectra. HSU aims at estimating the pure spectra present in the
scene of interest, referred to as endmembers, and their fractions in each
pixel, referred to as abundances. Today, many HSU algorithms have been
proposed, based either on a geometrical or statistical model. While most
methods assume that the number of endmembers present in the scene is known,
there is only little work about estimating this number from the observed data.
In this work, we propose a Bayesian nonparametric framework that jointly
estimates the number of endmembers, the endmembers itself, and their
abundances, by making use of the Indian Buffet Process as a prior for the
endmembers. Simulation results and experiments on real data demonstrate the
effectiveness of the proposed algorithm, yielding results comparable with
state-of-the-art methods while being able to reliably infer the number of
endmembers. In scenarios with strong noise, where other algorithms provide only
poor results, the proposed approach tends to overestimate the number of
endmembers slightly. The additional endmembers, however, often simply represent
noisy replicas of present endmembers and could easily be merged in a
post-processing step
Bayesian Nonparametric Feature and Policy Learning for Decision-Making
Learning from demonstrations has gained increasing interest in the recent
past, enabling an agent to learn how to make decisions by observing an
experienced teacher. While many approaches have been proposed to solve this
problem, there is only little work that focuses on reasoning about the observed
behavior. We assume that, in many practical problems, an agent makes its
decision based on latent features, indicating a certain action. Therefore, we
propose a generative model for the states and actions. Inference reveals the
number of features, the features, and the policies, allowing us to learn and to
analyze the underlying structure of the observed behavior. Further, our
approach enables prediction of actions for new states. Simulations are used to
assess the performance of the algorithm based upon this model. Moreover, the
problem of learning a driver's behavior is investigated, demonstrating the
performance of the proposed model in a real-world scenario