62,779 research outputs found
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient
distributed optimization methods for machine learning. We present a
general-purpose framework for distributed computing environments, CoCoA, that
has an efficient communication scheme and is applicable to a wide variety of
problems in machine learning and signal processing. We extend the framework to
cover general non-strongly-convex regularizers, including L1-regularized
problems like lasso, sparse logistic regression, and elastic net
regularization, and show how earlier work can be derived as a special case. We
provide convergence guarantees for the class of convex regularized loss
minimization objectives, leveraging a novel approach in handling
non-strongly-convex regularizers and non-smooth loss functions. The resulting
framework has markedly improved performance over state-of-the-art methods, as
we illustrate with an extensive set of experiments on real distributed
datasets
Sparsity Based Poisson Denoising with Dictionary Learning
The problem of Poisson denoising appears in various imaging applications,
such as low-light photography, medical imaging and microscopy. In cases of high
SNR, several transformations exist so as to convert the Poisson noise into an
additive i.i.d. Gaussian noise, for which many effective algorithms are
available. However, in a low SNR regime, these transformations are
significantly less accurate, and a strategy that relies directly on the true
noise statistics is required. A recent work by Salmon et al. took this route,
proposing a patch-based exponential image representation model based on GMM
(Gaussian mixture model), leading to state-of-the-art results. In this paper,
we propose to harness sparse-representation modeling to the image patches,
adopting the same exponential idea. Our scheme uses a greedy pursuit with
boot-strapping based stopping condition and dictionary learning within the
denoising process. The reconstruction performance of the proposed scheme is
competitive with leading methods in high SNR, and achieving state-of-the-art
results in cases of low SNR.Comment: 13 pages, 9 figure
-Box Optimization for Green Cloud-RAN via Network Adaptation
In this paper, we propose a reformulation for the Mixed Integer Programming
(MIP) problem into an exact and continuous model through using the -box
technique to recast the binary constraints into a box with an sphere
constraint. The reformulated problem can be tackled by a dual ascent algorithm
combined with a Majorization-Minimization (MM) method for the subproblems to
solve the network power consumption problem of the Cloud Radio Access Network
(Cloud-RAN), and which leads to solving a sequence of Difference of Convex (DC)
subproblems handled by an inexact MM algorithm. After obtaining the final
solution, we use it as the initial result of the bi-section Group Sparse
Beamforming (GSBF) algorithm to promote the group-sparsity of beamformers,
rather than using the weighted -norm. Simulation results
indicate that the new method outperforms the bi-section GSBF algorithm by
achieving smaller network power consumption, especially in sparser cases, i.e.,
Cloud-RANs with a lot of Remote Radio Heads (RRHs) but fewer users.Comment: 4 pages, 4 figure
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