5,085 research outputs found
A Dantzig Selector Approach to Temporal Difference Learning
LSTD is a popular algorithm for value function approximation. Whenever the
number of features is larger than the number of samples, it must be paired with
some form of regularization. In particular, L1-regularization methods tend to
perform feature selection by promoting sparsity, and thus, are well-suited for
high-dimensional problems. However, since LSTD is not a simple regression
algorithm, but it solves a fixed--point problem, its integration with
L1-regularization is not straightforward and might come with some drawbacks
(e.g., the P-matrix assumption for LASSO-TD). In this paper, we introduce a
novel algorithm obtained by integrating LSTD with the Dantzig Selector. We
investigate the performance of the proposed algorithm and its relationship with
the existing regularized approaches, and show how it addresses some of their
drawbacks.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
A Regularized Method for Selecting Nested Groups of Relevant Genes from Microarray Data
Gene expression analysis aims at identifying the genes able to accurately
predict biological parameters like, for example, disease subtyping or
progression. While accurate prediction can be achieved by means of many
different techniques, gene identification, due to gene correlation and the
limited number of available samples, is a much more elusive problem. Small
changes in the expression values often produce different gene lists, and
solutions which are both sparse and stable are difficult to obtain. We propose
a two-stage regularization method able to learn linear models characterized by
a high prediction performance. By varying a suitable parameter these linear
models allow to trade sparsity for the inclusion of correlated genes and to
produce gene lists which are almost perfectly nested. Experimental results on
synthetic and microarray data confirm the interesting properties of the
proposed method and its potential as a starting point for further biological
investigationsComment: 17 pages, 8 Post-script figure
Non-convex regularization in remote sensing
In this paper, we study the effect of different regularizers and their
implications in high dimensional image classification and sparse linear
unmixing. Although kernelization or sparse methods are globally accepted
solutions for processing data in high dimensions, we present here a study on
the impact of the form of regularization used and its parametrization. We
consider regularization via traditional squared (2) and sparsity-promoting (1)
norms, as well as more unconventional nonconvex regularizers (p and Log Sum
Penalty). We compare their properties and advantages on several classification
and linear unmixing tasks and provide advices on the choice of the best
regularizer for the problem at hand. Finally, we also provide a fully
functional toolbox for the community.Comment: 11 pages, 11 figure
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
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