Special Issue "Dictionary Learning Algorithms and Applications"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: 15 May 2019
Sparse representations have found numerous applications in signal and image processing, coding, compression, classification, modeling and other fields. Their success relies on the parsimony principle: few members of an overcomplete basis can offer a large variety of models. The overcomplete basis, or dictionary, can be fixed or adapted to the application.
Dictionary learning is the technique of designing dictionaries based on samples from the process to be modeled. In many applications, learned dictionaries offer better performance than fixed ones. There are already well established algorithms for the standard problem, but the topic is still open for variations of the learning problem and especially for applications.
We invite you to submit high quality papers to this issue on “Dictionary learning algorithms and applications”, with subjects covering the whole range from theory to applications. The topics include, but are not limited to:
- Dictionary learning (DL) algorithms and toolboxes
- Theoretical properties of DL algorithms
- New formulations and solutions of the DL problem
- Structured dictionary learning
- Manifold dictionary learning
- Kernel dictionary learning
- Incoherent frames
- Classification using sparse representations and DL
- DL applications in signal processing, machine learning, and generally in all engineering fields
Prof. Bogdan Dumitrescu
Dr. Cristian Rusu
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Dictionary learning
- Sparse representations
- Signal and image processing
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Dictionary learning for zonotopic approximations of multi-obstacle environments
Author: Florin Stoican, Paul Irofti
Affliation: Deptment of Automatic Control and Systems Engineering, University Politehnica of Bucharest, Romania
Abstract: Control problems involving autonomous vehicles often require a priori knowledge about existing features (obstacles, restricted or target areas, etc.). Therefore, the description of the environment is of paramount importance for the subsequent motion planning strategies. We propose the approximation of the regions of interest through a collection of zonotopic sets whose center and scaling are the result of dictionary learning procedure whose goal is to minimize the complexity of the representation while also providing a close approximation of the initial representation.