1,561 research outputs found
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
KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
We consider the image classification problem via kernel collaborative
representation classification with locality constrained dictionary (KCRC-LCD).
Specifically, we propose a kernel collaborative representation classification
(KCRC) approach in which kernel method is used to improve the discrimination
ability of collaborative representation classification (CRC). We then measure
the similarities between the query and atoms in the global dictionary in order
to construct a locality constrained dictionary (LCD) for KCRC. In addition, we
discuss several similarity measure approaches in LCD and further present a
simple yet effective unified similarity measure whose superiority is validated
in experiments. There are several appealing aspects associated with LCD. First,
LCD can be nicely incorporated under the framework of KCRC. The LCD similarity
measure can be kernelized under KCRC, which theoretically links CRC and LCD
under the kernel method. Second, KCRC-LCD becomes more scalable to both the
training set size and the feature dimension. Example shows that KCRC is able to
perfectly classify data with certain distribution, while conventional CRC fails
completely. Comprehensive experiments on many public datasets also show that
KCRC-LCD is a robust discriminative classifier with both excellent performance
and good scalability, being comparable or outperforming many other
state-of-the-art approaches
Steady-State Performance of an Adaptive Combined MISO Filter Using the Multichannel Affine Projection Algorithm
The combination of adaptive filters is an effective approach to improve filtering performance. In this paper, we investigate the performance of an adaptive combined scheme between two adaptive multiple-input single-output (MISO) filters, which can be easily extended to the case of
multiple outputs. In order to generalize the analysis, we consider the multichannel affine projection
algorithm (APA) to update the coefficients of the MISO filters, which increases the possibility of
exploiting the capabilities of the filtering scheme. Using energy conservation relations, we derive
a theoretical behavior of the proposed adaptive combination scheme at steady state. Such analysis
entails some further theoretical insights with respect to the single channel combination scheme.
Simulation results prove both the validity of the theoretical steady-state analysis and the effectiveness
of the proposed combined scheme.The work of Danilo Comminiello, Michele Scarpiniti and Aurelio Uncini has been supported by the
project: “Vehicular Fog energy-efficient QoS mining and dissemination of multimedia Big Data streams (V-FoG
and V-Fog2)”, funded by Sapienza University of Rome Bando 2016 and 2017. The work of Michele Scarpiniti
and Aurelio Uncini has been also supported by the project: “GAUChO – A Green Adaptive Fog Computing and
networking Architectures” funded by the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando
2015, grant 2015YPXH4W_004. The work of Luis A. Azpicueta-Ruiz is partially supported by the Spanish Ministry
of Economy and Competitiveness (under grant DAMA (TIN2015-70308-REDT) and grants TEC2014-52289-R and
TEC2017-83838-R), and by the European Union
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