7,258 research outputs found
Dual Averaging Method for Online Graph-structured Sparsity
Online learning algorithms update models via one sample per iteration, thus
efficient to process large-scale datasets and useful to detect malicious events
for social benefits, such as disease outbreak and traffic congestion on the
fly. However, existing algorithms for graph-structured models focused on the
offline setting and the least square loss, incapable for online setting, while
methods designed for online setting cannot be directly applied to the problem
of complex (usually non-convex) graph-structured sparsity model. To address
these limitations, in this paper we propose a new algorithm for
graph-structured sparsity constraint problems under online setting, which we
call \textsc{GraphDA}. The key part in \textsc{GraphDA} is to project both
averaging gradient (in dual space) and primal variables (in primal space) onto
lower dimensional subspaces, thus capturing the graph-structured sparsity
effectively. Furthermore, the objective functions assumed here are generally
convex so as to handle different losses for online learning settings. To the
best of our knowledge, \textsc{GraphDA} is the first online learning algorithm
for graph-structure constrained optimization problems. To validate our method,
we conduct extensive experiments on both benchmark graph and real-world graph
datasets. Our experiment results show that, compared to other baseline methods,
\textsc{GraphDA} not only improves classification performance, but also
successfully captures graph-structured features more effectively, hence
stronger interpretability.Comment: 11 pages, 14 figure
A Unified View of Large-scale Zero-sum Equilibrium Computation
The task of computing approximate Nash equilibria in large zero-sum
extensive-form games has received a tremendous amount of attention due mainly
to the Annual Computer Poker Competition. Immediately after its inception, two
competing and seemingly different approaches emerged---one an application of
no-regret online learning, the other a sophisticated gradient method applied to
a convex-concave saddle-point formulation. Since then, both approaches have
grown in relative isolation with advancements on one side not effecting the
other. In this paper, we rectify this by dissecting and, in a sense, unify the
two views.Comment: AAAI Workshop on Computer Poker and Imperfect Informatio
A Stochastic Majorize-Minimize Subspace Algorithm for Online Penalized Least Squares Estimation
Stochastic approximation techniques play an important role in solving many
problems encountered in machine learning or adaptive signal processing. In
these contexts, the statistics of the data are often unknown a priori or their
direct computation is too intensive, and they have thus to be estimated online
from the observed signals. For batch optimization of an objective function
being the sum of a data fidelity term and a penalization (e.g. a sparsity
promoting function), Majorize-Minimize (MM) methods have recently attracted
much interest since they are fast, highly flexible, and effective in ensuring
convergence. The goal of this paper is to show how these methods can be
successfully extended to the case when the data fidelity term corresponds to a
least squares criterion and the cost function is replaced by a sequence of
stochastic approximations of it. In this context, we propose an online version
of an MM subspace algorithm and we study its convergence by using suitable
probabilistic tools. Simulation results illustrate the good practical
performance of the proposed algorithm associated with a memory gradient
subspace, when applied to both non-adaptive and adaptive filter identification
problems
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