This work was supported by the Queen Mary University of London School Studentship, the EU FET-Open project FP7-
ICT-225913-SMALL. Sparse Models, Algorithms and Learning for Large-scale data and a Leadership Fellowship from the UK
Engineering and Physical Sciences Research Council (EPSRC)

Barchiesi, Daniele

Plumbley, Mark

English

Daniele Barchiesi

Mark D. Plumbley

Crossref

IEEE Transactions on Signal Processing

Learning Incoherent Dictionaries for Sparse Approximation Using Iterative Projections and Rotations

Queen Mary Research Online

Adaptive greedy approximations.

Atomic decomposition by basis pursuit.

Average performance analysis for thresholding.

Computational methods for sparse solution of linear inverse problems.

CoSaMP: Iterative signal recovery from incomplete and inaccurate samples.

Designing structured tight frames via an alternating projection method.

Dictionaries for sparse representation modeling.

Dictionary preconditioning for greedy algorithms.

Geometrical methods for non-negative ICA: Manifolds, Lie groups and toral subalgebras.

Grassmannian frames with applications to coding and communication.

Greed is good: Algorithmic results for sparse approximation.

INK-SVD: Learning incoherent dictionaries for sparse representations. To appear

K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation.

Learning overcomplete representations.

Lie-group methods.

Method of optimal directions for frame design.

On the conditioning of random subdictionaries.

Online learning for matrix factorization and sparse coding.

Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition.

Recursive least squares dictionary learning algorithm.

Self-stabilized gradient algorithms for blind source separation with orthogonality constraints.

SMALLbox - An evaluation framework for sparse representations and dictionary learning algorithms.

Sparse modeling with universal priors and learned incoherent dictionaries.

Uniform uncertanty principle and signal recovery via regularized orthogonal matching pursuit.

Learning incoherent dictionaries for sparse approximation using iterative projections and rotations

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Barchiesi, D

Plumbley, MD

An introduction to compressive sampling.

Closed-form solution of absolute orientation using orthonormal matrices.

Dictionary identification: Sparse matrixfactorisation via `1-minimisation.

Dictionary learning and update based on simultaneous codeword optimization (SIMCO).

Dictionary learning for the sparse approximation of audio signals.

Efficient auditory coding.

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INK-SVD: Learning incoherent dictionaries for sparse representations.

Iterative thresholding for sparse approximations.

Local optimality of dictionary learning algorithms.

Matching pursuit with time-frequency dictionaries.

Morphological component analysis: An adaptive thresholding strategy.

Nonlinear approximation.

On the exponential convergence of matching pursuit in quasi-incoherent dictionaries.

On the local correctness of `1-minimization for dictionary learning. available at http://arxiv.org/pdf/1101.5672.pdf,

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Parametric dictionary design for sparse coding.

This article deals with learning dictionaries for sparse approximation whose atoms are both adapted to a training set of signals and mutually incoherent. To meet this objective, we employ a dictionary learning scheme consisting of sparse approximation followed by dictionary update and we add to the latter a decorrelation step in order to reach a target mutual coherence level. This step is accomplished by an iterative projection method complemented by a rotation of the dictionary. Experiments on musical audio data and a comparison with the method of optimal coherence-constrained directions (mocod) and the incoherent k-svd (ink-svd) illustrate that the proposed algorithm can learn dictionaries that exhibit a low mutual coherence while providing a sparse approximation with better signal-to-noise ratio (snr) than the benchmark technique

Surrey Research Insight

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.3411

Learning incoherent dictionaries for sparse approximation using iterative projections and rotations

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