487 research outputs found
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling
Conditional Random Fields (CRFs) constitute a popular and efficient approach
for supervised sequence labelling. CRFs can cope with large description spaces
and can integrate some form of structural dependency between labels. In this
contribution, we address the issue of efficient feature selection for CRFs
based on imposing sparsity through an L1 penalty. We first show how sparsity of
the parameter set can be exploited to significantly speed up training and
labelling. We then introduce coordinate descent parameter update schemes for
CRFs with L1 regularization. We finally provide some empirical comparisons of
the proposed approach with state-of-the-art CRF training strategies. In
particular, it is shown that the proposed approach is able to take profit of
the sparsity to speed up processing and hence potentially handle larger
dimensional models
Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
In recent years a number of methods have been developed for automatically
learning the (sparse) connectivity structure of Markov Random Fields. These
methods are mostly based on L1-regularized optimization which has a number of
disadvantages such as the inability to assess model uncertainty and expensive
cross-validation to find the optimal regularization parameter. Moreover, the
model's predictive performance may degrade dramatically with a suboptimal value
of the regularization parameter (which is sometimes desirable to induce
sparseness). We propose a fully Bayesian approach based on a "spike and slab"
prior (similar to L0 regularization) that does not suffer from these
shortcomings. We develop an approximate MCMC method combining Langevin dynamics
and reversible jump MCMC to conduct inference in this model. Experiments show
that the proposed model learns a good combination of the structure and
parameter values without the need for separate hyper-parameter tuning.
Moreover, the model's predictive performance is much more robust than L1-based
methods with hyper-parameter settings that induce highly sparse model
structures.Comment: Accepted in the Conference on Uncertainty in Artificial Intelligence
(UAI), 201
Sparse Regression with Multi-type Regularized Feature Modeling
Within the statistical and machine learning literature, regularization
techniques are often used to construct sparse (predictive) models. Most
regularization strategies only work for data where all predictors are treated
identically, such as Lasso regression for (continuous) predictors treated as
linear effects. However, many predictive problems involve different types of
predictors and require a tailored regularization term. We propose a multi-type
Lasso penalty that acts on the objective function as a sum of subpenalties, one
for each type of predictor. As such, we allow for predictor selection and level
fusion within a predictor in a data-driven way, simultaneous with the parameter
estimation process. We develop a new estimation strategy for convex predictive
models with this multi-type penalty. Using the theory of proximal operators,
our estimation procedure is computationally efficient, partitioning the overall
optimization problem into easier to solve subproblems, specific for each
predictor type and its associated penalty. Earlier research applies
approximations to non-differentiable penalties to solve the optimization
problem. The proposed SMuRF algorithm removes the need for approximations and
achieves a higher accuracy and computational efficiency. This is demonstrated
with an extensive simulation study and the analysis of a case-study on
insurance pricing analytics
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