Matching pursuits with time-frequency dictionaries

By Stéphane Mallat, Zhifeng Zhang. 1993

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

Citations in the corpus

Convolutional feature masking for joint object and stuff segmentation (2014, methodology): “To generate one compact combination from this candidate set, we adopt a procedure similar to the matching pursuit [23, 17].”
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition (2010, methodology): “Matching Pursuit methods [9] offer a greedy approximation t this problem.”
Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso (2017, neutral): “, 2011); greedy alternatives to forward stepwise algorithm, like matching pursuit (Mallat and Zhang, 1993) and orthogonal matching pursuit (Davis et al.”
Stability selection (2008, background): “A drawback of OMP is clearly that conditions for consistent variable selection are quite strong.”
Multi-Label Prediction via Compressed Sensing (2009, methodology): “Orthogonal Matching Pursuit (OMP) [18], FoBa [19], and CoSaMP [20] are examples of iterative or greedy reconstruct ion algorithms.”
Highly sparse representations from dictionaries are unique and independent of the sparseness measure (2007, unknown): “The approach –or variants thereof– has been used successfully with the Gabor multiscale dictionary [36], the union of a wavelet basis and a local Fourier basis for audio coding [8, 9], as well as with tight curvelet frames and local Fourier bases for image segmentation and source separation [42].”
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples (2008, unknown): “Mallat and Zhang introduced greedy algorithms into the signal processing literature and proposed the name matching pursuit [27].”
Construction of approximation spaces for reinforcement learning (2013, methodology): “One could use this bound to select the best feature set for a given MDP, similar to matching pursuit approaches (Mallat and Zhang, 1993).”
Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation (2010, methodology): “Indeed, techniques such as matching pursuit (MP) [2], orthogonal matching pursuit (OMP) [13], stagewise orthogonal matching pursuit (StOMP) [16], and their variants, all involve costly dictionary-signal computations each iteration.”
Control of Fast-Reaching Movements by Muscle Synergy Combinations (2006, methodology): “…by iterating the following three steps: (1) given a set of synergies, find the synergy onset times for each trial by a matching pursuit procedure (Mallat and Zhang, 1993; d’Avella and Bizzi, 2005); (2) given a set of synergies and their onset times, find the non-negative scaling coefficients for…”
Wavelets and Subband Coding (2013, background): “Representations involving both scale and modulation are discussed in [185, 291].”
Subset selection in noise based on diversity measure minimization (2003, background): “There are many solutions to the system of equations in (1) and the subset selection problem corresponds to identifying a few columns of the matrix , which can be used to represent the data vector [1], [2], [14].”