22,597 research outputs found
Estimation of the Number of Spikes, Possibly Equal, in the High-Dimensional Case
Estimating the number of spikes in a spiked model is an important problem in
many areas such as signal processing. Most of the classical approaches assume a
large sample size whereas the dimension of the observations is kept
small. In this paper, we consider the case of high dimension, where is
large compared to . The approach is based on recent results of random matrix
theory. We extend our previous results to a more difficult situation where some
spikes are equal, and compare our algorithm to an existing benchmark method
Super-Resolution of Positive Sources: the Discrete Setup
In single-molecule microscopy it is necessary to locate with high precision
point sources from noisy observations of the spectrum of the signal at
frequencies capped by , which is just about the frequency of natural
light. This paper rigorously establishes that this super-resolution problem can
be solved via linear programming in a stable manner. We prove that the quality
of the reconstruction crucially depends on the Rayleigh regularity of the
support of the signal; that is, on the maximum number of sources that can occur
within a square of side length about . The theoretical performance
guarantee is complemented with a converse result showing that our simple convex
program convex is nearly optimal. Finally, numerical experiments illustrate our
methods.Comment: 31 page, 7 figure
Convexity in source separation: Models, geometry, and algorithms
Source separation or demixing is the process of extracting multiple
components entangled within a signal. Contemporary signal processing presents a
host of difficult source separation problems, from interference cancellation to
background subtraction, blind deconvolution, and even dictionary learning.
Despite the recent progress in each of these applications, advances in
high-throughput sensor technology place demixing algorithms under pressure to
accommodate extremely high-dimensional signals, separate an ever larger number
of sources, and cope with more sophisticated signal and mixing models. These
difficulties are exacerbated by the need for real-time action in automated
decision-making systems.
Recent advances in convex optimization provide a simple framework for
efficiently solving numerous difficult demixing problems. This article provides
an overview of the emerging field, explains the theory that governs the
underlying procedures, and surveys algorithms that solve them efficiently. We
aim to equip practitioners with a toolkit for constructing their own demixing
algorithms that work, as well as concrete intuition for why they work
Understanding the fine structure of electricity prices
This paper analyzes the special features of electricity spot prices derived from the physics of this commodity and from the economics of supply and demand in a market pool. Besides mean reversion, a property they share with other commodities, power prices exhibit the unique feature of spikes in trajectories. We introduce a class of discontinuous processes exhibiting a "jump-reversion" component to properly represent these sharp upward moves shortly followed by drops of similar magnitude. Our approach allows to capture—for the first time to our knowledge—both the trajectorial and the statistical properties of electricity pool prices. The quality of the fitting is illustrated on a database of major U.S. power markets
Structured Sparsity: Discrete and Convex approaches
Compressive sensing (CS) exploits sparsity to recover sparse or compressible
signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity
is also used to enhance interpretability in machine learning and statistics
applications: While the ambient dimension is vast in modern data analysis
problems, the relevant information therein typically resides in a much lower
dimensional space. However, many solutions proposed nowadays do not leverage
the true underlying structure. Recent results in CS extend the simple sparsity
idea to more sophisticated {\em structured} sparsity models, which describe the
interdependency between the nonzero components of a signal, allowing to
increase the interpretability of the results and lead to better recovery
performance. In order to better understand the impact of structured sparsity,
in this chapter we analyze the connections between the discrete models and
their convex relaxations, highlighting their relative advantages. We start with
the general group sparse model and then elaborate on two important special
cases: the dispersive and the hierarchical models. For each, we present the
models in their discrete nature, discuss how to solve the ensuing discrete
problems and then describe convex relaxations. We also consider more general
structures as defined by set functions and present their convex proxies.
Further, we discuss efficient optimization solutions for structured sparsity
problems and illustrate structured sparsity in action via three applications.Comment: 30 pages, 18 figure
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