6,038 research outputs found
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Due to its causal semantics, Bayesian networks (BN) have been widely employed
to discover the underlying data relationship in exploratory studies, such as
brain research. Despite its success in modeling the probability distribution of
variables, BN is naturally a generative model, which is not necessarily
discriminative. This may cause the ignorance of subtle but critical network
changes that are of investigation values across populations. In this paper, we
propose to improve the discriminative power of BN models for continuous
variables from two different perspectives. This brings two general
discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the
first framework, we employ Fisher kernel to bridge the generative models of GBN
and the discriminative classifiers of SVMs, and convert the GBN parameter
learning to Fisher kernel learning via minimizing a generalization error bound
of SVMs. In the second framework, we employ the max-margin criterion and build
it directly upon GBN models to explicitly optimize the classification
performance of the GBNs. The advantages and disadvantages of the two frameworks
are discussed and experimentally compared. Both of them demonstrate strong
power in learning discriminative parameters of GBNs for neuroimaging based
brain network analysis, as well as maintaining reasonable representation
capacity. The contributions of this paper also include a new Directed Acyclic
Graph (DAG) constraint with theoretical guarantee to ensure the graph validity
of GBN.Comment: 16 pages and 5 figures for the article (excluding appendix
Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood
We consider the problem of discriminative factor analysis for data that are
in general non-Gaussian. A Bayesian model based on the ranks of the data is
proposed. We first introduce a new {\em max-margin} version of the
rank-likelihood. A discriminative factor model is then developed, integrating
the max-margin rank-likelihood and (linear) Bayesian support vector machines,
which are also built on the max-margin principle. The discriminative factor
model is further extended to the {\em nonlinear} case through mixtures of local
linear classifiers, via Dirichlet processes. Fully local conjugacy of the model
yields efficient inference with both Markov Chain Monte Carlo and variational
Bayes approaches. Extensive experiments on benchmark and real data demonstrate
superior performance of the proposed model and its potential for applications
in computational biology.Comment: 14 pages, 7 figures, ICML 201
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