9,227 research outputs found
Bounded Coordinate-Descent for Biological Sequence Classification in High Dimensional Predictor Space
We present a framework for discriminative sequence classification where the
learner works directly in the high dimensional predictor space of all
subsequences in the training set. This is possible by employing a new
coordinate-descent algorithm coupled with bounding the magnitude of the
gradient for selecting discriminative subsequences fast. We characterize the
loss functions for which our generic learning algorithm can be applied and
present concrete implementations for logistic regression (binomial
log-likelihood loss) and support vector machines (squared hinge loss).
Application of our algorithm to protein remote homology detection and remote
fold recognition results in performance comparable to that of state-of-the-art
methods (e.g., kernel support vector machines). Unlike state-of-the-art
classifiers, the resulting classification models are simply lists of weighted
discriminative subsequences and can thus be interpreted and related to the
biological problem
Optimization with Sparsity-Inducing Penalties
Sparse estimation methods are aimed at using or obtaining parsimonious
representations of data or models. They were first dedicated to linear variable
selection but numerous extensions have now emerged such as structured sparsity
or kernel selection. It turns out that many of the related estimation problems
can be cast as convex optimization problems by regularizing the empirical risk
with appropriate non-smooth norms. The goal of this paper is to present from a
general perspective optimization tools and techniques dedicated to such
sparsity-inducing penalties. We cover proximal methods, block-coordinate
descent, reweighted -penalized techniques, working-set and homotopy
methods, as well as non-convex formulations and extensions, and provide an
extensive set of experiments to compare various algorithms from a computational
point of view
Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed
environments, e.g., sensor networks or peer-to-peer protocols. In these
scenarios, the agents in the environment receive measurements in a streaming
fashion, and they are required to estimate a common (nonlinear) model by
alternating local computations and communications with their neighbors. We
focus on the important distinction between single-task problems, where the
underlying model is common to all agents, and multitask problems, where each
agent might converge to a different model due to, e.g., spatial dependencies or
other factors. Currently, most of the literature on distributed learning in the
nonlinear case has focused on the single-task case, which may be a strong
limitation in real-world scenarios. After introducing the problem and reviewing
the existing approaches, we describe a simple kernel-based algorithm tailored
for the multitask case. We evaluate the proposal on a simulated benchmark task,
and we conclude by detailing currently open problems and lines of research.Comment: To be published as a chapter in `Adaptive Learning Methods for
Nonlinear System Modeling', Elsevier Publishing, Eds. D. Comminiello and J.C.
Principe (2018
- …