566 research outputs found
Computational Intractability of Dictionary Learning for Sparse Representation
In this paper we consider the dictionary learning problem for sparse
representation. We first show that this problem is NP-hard by polynomial time
reduction of the densest cut problem. Then, using successive convex
approximation strategies, we propose efficient dictionary learning schemes to
solve several practical formulations of this problem to stationary points.
Unlike many existing algorithms in the literature, such as K-SVD, our proposed
dictionary learning scheme is theoretically guaranteed to converge to the set
of stationary points under certain mild assumptions. For the image denoising
application, the performance and the efficiency of the proposed dictionary
learning scheme are comparable to that of K-SVD algorithm in simulation
A Bayesian Framework for Sparse Representation-Based 3D Human Pose Estimation
A Bayesian framework for 3D human pose estimation from monocular images based
on sparse representation (SR) is introduced. Our probabilistic approach aims at
simultaneously learning two overcomplete dictionaries (one for the visual input
space and the other for the pose space) with a shared sparse representation.
Existing SR-based pose estimation approaches only offer a point estimation of
the dictionary and the sparse codes. Therefore, they might be unreliable when
the number of training examples is small. Our Bayesian framework estimates a
posterior distribution for the sparse codes and the dictionaries from labeled
training data. Hence, it is robust to overfitting on small-size training data.
Experimental results on various human activities show that the proposed method
is superior to the state of-the-art pose estimation algorithms.Comment: Accepted in IEEE Signal Processing Letters (SPL), 201
Approximate Guarantees for Dictionary Learning
In the dictionary learning (or sparse coding) problem, we are given a
collection of signals (vectors in ), and the goal is to find a
"basis" in which the signals have a sparse (approximate) representation. The
problem has received a lot of attention in signal processing, learning, and
theoretical computer science. The problem is formalized as factorizing a matrix
(whose columns are the signals) as , where has a
prescribed number of columns (typically ), and has columns
that are -sparse (typically ). Most of the known theoretical
results involve assuming that the columns of the unknown have certain
incoherence properties, and that the coefficient matrix has random (or
partly random) structure.
The goal of our work is to understand what can be said in the absence of such
assumptions. Can we still find and such that ? We show
that this is possible, if we allow violating the bounds on and by
appropriate factors that depend on and the desired approximation. Our
results rely on an algorithm for what we call the threshold correlation
problem, which turns out to be related to hypercontractive norms of matrices.
We also show that our algorithmic ideas apply to a setting in which some of the
columns of are outliers, thus giving similar guarantees even in this
challenging setting.Comment: Accepted for presentation at the Conference on Learning Theory (COLT)
201
More Algorithms for Provable Dictionary Learning
In dictionary learning, also known as sparse coding, the algorithm is given
samples of the form where is an unknown random
sparse vector and is an unknown dictionary matrix in (usually , which is the overcomplete case). The goal is to learn
and . This problem has been studied in neuroscience, machine learning,
visions, and image processing. In practice it is solved by heuristic algorithms
and provable algorithms seemed hard to find. Recently, provable algorithms were
found that work if the unknown feature vector is -sparse or even
sparser. Spielman et al. \cite{DBLP:journals/jmlr/SpielmanWW12} did this for
dictionaries where ; Arora et al. \cite{AGM} gave an algorithm for
overcomplete () and incoherent matrices ; and Agarwal et al.
\cite{DBLP:journals/corr/AgarwalAN13} handled a similar case but with weaker
guarantees.
This raised the problem of designing provable algorithms that allow sparsity
in the hidden vector . The current paper designs algorithms
that allow sparsity up to . It works for a class of matrices
where features are individually recoverable, a new notion identified in this
paper that may motivate further work.
The algorithm runs in quasipolynomial time because they use limited
enumeration.Comment: 23 page
Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing
This paper proposes a CS scheme that exploits the representational power of
restricted Boltzmann machines and deep learning architectures to model the
prior distribution of the sparsity pattern of signals belonging to the same
class. The determined probability distribution is then used in a maximum a
posteriori (MAP) approach for the reconstruction. The parameters of the prior
distribution are learned from training data. The motivation behind this
approach is to model the higher-order statistical dependencies between the
coefficients of the sparse representation, with the final goal of improving the
reconstruction. The performance of the proposed method is validated on the
Berkeley Segmentation Dataset and the MNIST Database of handwritten digits.Comment: Accepted for publication at IEEE Transactions on Signal Processin
On the uniqueness and stability of dictionaries for sparse representation of noisy signals
Learning optimal dictionaries for sparse coding has exposed characteristic
sparse features of many natural signals. However, universal guarantees of the
stability of such features in the presence of noise are lacking. Here, we
provide very general conditions guaranteeing when dictionaries yielding the
sparsest encodings are unique and stable with respect to measurement or
modeling error. We demonstrate that some or all original dictionary elements
are recoverable from noisy data even if the dictionary fails to satisfy the
spark condition, its size is overestimated, or only a polynomial number of
distinct sparse supports appear in the data. Importantly, we derive these
guarantees without requiring any constraints on the recovered dictionary beyond
a natural upper bound on its size. Our results also yield an effective
procedure sufficient to affirm if a proposed solution to the dictionary
learning problem is unique within bounds commensurate with the noise. We
suggest applications to data analysis, engineering, and neuroscience and close
with some remaining challenges left open by our work
Image Super-Resolution via Sparse Bayesian Modeling of Natural Images
Image super-resolution (SR) is one of the long-standing and active topics in
image processing community. A large body of works for image super resolution
formulate the problem with Bayesian modeling techniques and then obtain its
Maximum-A-Posteriori (MAP) solution, which actually boils down to a regularized
regression task over separable regularization term. Although straightforward,
this approach cannot exploit the full potential offered by the probabilistic
modeling, as only the posterior mode is sought. Also, the separable property of
the regularization term can not capture any correlations between the sparse
coefficients, which sacrifices much on its modeling accuracy. We propose a
Bayesian image SR algorithm via sparse modeling of natural images. The sparsity
property of the latent high resolution image is exploited by introducing latent
variables into the high-order Markov Random Field (MRF) which capture the
content adaptive variance by pixel-wise adaptation. The high-resolution image
is estimated via Empirical Bayesian estimation scheme, which is substantially
faster than our previous approach based on Markov Chain Monte Carlo sampling
[1]. It is shown that the actual cost function for the proposed approach
actually incorporates a non-factorial regularization term over the sparse
coefficients. Experimental results indicate that the proposed method can
generate competitive or better results than \emph{state-of-the-art} SR
algorithms.Comment: 8 figures, 29 page
A first approach to learning a best basis for gravitational field modelling
Gravitational field modelling is an important tool for inferring past and
present dynamic processes of the Earth. Functions on the sphere such as the
gravitational potential are usually expanded in terms of either spherical
harmonics or radial basis functions (RBFs). The (Regularized) Functional
Matching Pursuit ((R)FMP) and its variants use an overcomplete dictionary of
diverse trial functions to build a best basis as a sparse subset of the
dictionary and compute a model, for instance, of the gravity field, in this
best basis. Thus, one advantage is that the dictionary may contain spherical
harmonics and RBFs. Moreover, these methods represent a possibility to obtain
an approximative and stable solution of an ill-posed inverse problem, such as
the downward continuation of gravitational data from the satellite orbit to the
Earth's surface, but also other inverse problems in geomathematics and medical
imaging. A remaining drawback is that in practice, the dictionary has to be
finite and, so far, could only be chosen by rule of thumb or trial-and-error.
In this paper, we develop a strategy for automatically choosing a dictionary by
a novel learning approach. We utilize a non-linear constrained optimization
problem to determine best-fitting RBFs (Abel-Poisson kernels). For this, we use
the Ipopt software package with an HSL subroutine. Details of the algorithm are
explained and first numerical results are shown
Convolutional Dictionary Learning: A Comparative Review and New Algorithms
Convolutional sparse representations are a form of sparse representation with
a dictionary that has a structure that is equivalent to convolution with a set
of linear filters. While effective algorithms have recently been developed for
the convolutional sparse coding problem, the corresponding dictionary learning
problem is substantially more challenging. Furthermore, although a number of
different approaches have been proposed, the absence of thorough comparisons
between them makes it difficult to determine which of them represents the
current state of the art. The present work both addresses this deficiency and
proposes some new approaches that outperform existing ones in certain contexts.
A thorough set of performance comparisons indicates a very wide range of
performance differences among the existing and proposed methods, and clearly
identifies those that are the most effective.Comment: Corrected typos in Eq. (18) and (19
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View
Extracting the underlying low-dimensional space where high-dimensional
signals often reside has long been at the center of numerous algorithms in the
signal processing and machine learning literature during the past few decades.
At the same time, working with incomplete (partly observed) large scale
datasets has recently been commonplace for diverse reasons. This so called {\it
big data era} we are currently living calls for devising online subspace
learning algorithms that can suitably handle incomplete data. Their envisaged
objective is to {\it recursively} estimate the unknown subspace by processing
streaming data sequentially, thus reducing computational complexity, while
obviating the need for storing the whole dataset in memory. In this paper, an
online variational Bayes subspace learning algorithm from partial observations
is presented. To account for the unawareness of the true rank of the subspace,
commonly met in practice, low-rankness is explicitly imposed on the sought
subspace data matrix by exploiting sparse Bayesian learning principles.
Moreover, sparsity, {\it simultaneously} to low-rankness, is favored on the
subspace matrix by the sophisticated hierarchical Bayesian scheme that is
adopted. In doing so, the proposed algorithm becomes adept in dealing with
applications whereby the underlying subspace may be also sparse, as, e.g., in
sparse dictionary learning problems. As shown, the new subspace tracking scheme
outperforms its state-of-the-art counterparts in terms of estimation accuracy,
in a variety of experiments conducted on simulated and real data
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