2,456 research outputs found
Fast Projection onto the Simplex and the l1 Ball
International audienceA new algorithm is proposed to project, exactly and in finite time, a vector of arbitrary size onto a simplex or an l1-norm ball. It can be viewed as a Gauss-Seidel-like variant of Michelot’s variable fixing algorithm; that is, the threshold used to fix the variables is updated after each element is read, instead of waiting for a full reading pass over the list of non-fixed elements. This algorithm is empirically demonstrated to be faster than existing methods
Projection Onto A Simplex
This mini-paper presents a fast and simple algorithm to compute the
projection onto the canonical simplex . Utilizing the Moreau's
identity, we show that the problem is essentially a univariate minimization and
the objective function is strictly convex and continuously differentiable.
Moreover, it is shown that there are at most n candidates which can be computed
explicitly, and the minimizer is the only one that falls into the correct
interval
High-Dimensional Menger-Type Curvatures - Part I: Geometric Multipoles and Multiscale Inequalities
We define a discrete Menger-type curvature of d+2 points in a real separable
Hilbert space H by an appropriate scaling of the squared volume of the
corresponding (d+1)-simplex. We then form a continuous curvature of an Ahlfors
d-regular measure on H by integrating the discrete curvature according to the
product measure. The aim of this work, continued in a subsequent paper, is to
estimate multiscale least squares approximations of such measures by the
Menger-type curvature. More formally, we show that the continuous d-dimensional
Menger-type curvature is comparable to the ``Jones-type flatness''. The latter
quantity adds up scaled errors of approximations of a measure by d-planes at
different scales and locations, and is commonly used to characterize uniform
rectifiability. We thus obtain a characterization of uniform rectifiability by
using the Menger-type curvature. In the current paper (part I) we control the
continuous Menger-type curvature of an Ahlfors d-regular measure by its
Jones-type flatness.Comment: 47 pages, 13 figures. Minor revisions and the inclusion of figure
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
A Practical Algorithm for Topic Modeling with Provable Guarantees
Topic models provide a useful method for dimensionality reduction and
exploratory data analysis in large text corpora. Most approaches to topic model
inference have been based on a maximum likelihood objective. Efficient
algorithms exist that approximate this objective, but they have no provable
guarantees. Recently, algorithms have been introduced that provide provable
bounds, but these algorithms are not practical because they are inefficient and
not robust to violations of model assumptions. In this paper we present an
algorithm for topic model inference that is both provable and practical. The
algorithm produces results comparable to the best MCMC implementations while
running orders of magnitude faster.Comment: 26 page
Regularized Optimal Transport and the Rot Mover's Distance
This paper presents a unified framework for smooth convex regularization of
discrete optimal transport problems. In this context, the regularized optimal
transport turns out to be equivalent to a matrix nearness problem with respect
to Bregman divergences. Our framework thus naturally generalizes a previously
proposed regularization based on the Boltzmann-Shannon entropy related to the
Kullback-Leibler divergence, and solved with the Sinkhorn-Knopp algorithm. We
call the regularized optimal transport distance the rot mover's distance in
reference to the classical earth mover's distance. We develop two generic
schemes that we respectively call the alternate scaling algorithm and the
non-negative alternate scaling algorithm, to compute efficiently the
regularized optimal plans depending on whether the domain of the regularizer
lies within the non-negative orthant or not. These schemes are based on
Dykstra's algorithm with alternate Bregman projections, and further exploit the
Newton-Raphson method when applied to separable divergences. We enhance the
separable case with a sparse extension to deal with high data dimensions. We
also instantiate our proposed framework and discuss the inherent specificities
for well-known regularizers and statistical divergences in the machine learning
and information geometry communities. Finally, we demonstrate the merits of our
methods with experiments using synthetic data to illustrate the effect of
different regularizers and penalties on the solutions, as well as real-world
data for a pattern recognition application to audio scene classification
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