Learning redundant dictionaries with translation invariance property: the MoTIF algorithm
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Wikipedia has become a widely-used resource on signal processing. However, the freelance-editing model of Wikipedia makes it challenging to maintain a high content quality. We develop techniques to monitor the network structure and content quality of Signa ...
Wikipedia has become a widely-used resource on signal processing. However, the freelance-editing model of Wikipedia makes it challenging to maintain a high content quality. We develop techniques to monitor the network structure and content quality of Signa ...
This is the second in a series of three columns summarizing the "Trends" expert sessions organized by the Signal Processing Society Technical Committees during ICASSP 2011 in Prague, Czech Republic. Readers have an opportunity to access these Trends sessio ...
In this work package (WP), we investigate the possibility of discovering structure within dictionary learning. This could range from exploring groups of atoms that appear in clusters - a form of molecule learning - to learning graphical dependencies across ...
In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in big data scenarios where multiple large dictionary models may be spr ...
Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...
Choosing a distance preserving measure or metric is fun- damental to many signal processing algorithms, such as k- means, nearest neighbor searches, hashing, and compressive sensing. In virtually all these applications, the efficiency of the signal process ...
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, bl ...
This paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal compon ...
Institute of Electrical and Electronics Engineers2012
This paper presents a new method for learning overcomplete dictionaries adapted to efficient joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms o ...
Institute of Electrical and Electronics Engineers2011
