Randomized Methods for Pattern Analysis
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 353
Special Issue Editors
Interests: machine learning; neural networks; computer vision; computational intelligence
Interests: machine learning; neural networks; stochastic optimization
Special Issue Information
Dear Colleagues,
Randomized methods have been proposed as an efficient and effective solution to address challenges of large-scale problems in machine learning, pattern recognition, data mining, neural computation, and related analysis tasks. Theoretical guarantees related to the universal approximation property of several randomized methods, as well as analysis related to their generalization capability, indicate that they are one of the leading paradigms for pattern analysis. Moreover, the power of randomized solutions has been illustrated in various problems, leading to state-of-the-art performance with reduced computational cost.
Randomized methods involve challenges and difficulties that usually do not appear in other learning paradigms, like gradient-based methods, such as the design of random mappings encoding domain knowledge, the design of effective training schemes, regularization schemes exploiting properties of the problem, the exploitation of the data structures using graphs, optimally combining multiple randomized mappings, and the design of efficient discriminative mappings. Despite the considerable number of existing works, several of the issues mentioned above remain open. This Special Issue seeks new contributions in Randomized methods and their applications in a wide range of pattern analysis problems. We have an especial interest in works focusing on the topics listed below, but works attending other approaches will also be well received.
Topics of interest to the Special Issue include (but are not limited to) the following:
- Randomized methods for large-scale pattern analysis;
- Regularization schemes for randomized methods;
- Sparsity methods based on randomized dictionaries;
- Randomized methods for matrix completion;
- Graph-based learning using randomized methods;
- Discriminative randomized methods;
- Subspace learning based on randomization;
- Randomized neural network training methods (RVFL, ELM, Random Kitchen Sinks, etc.);
- Hierarchical and deep learning based on randomization;
- Unsupervised and semi-supervised randomized methods;
- Ensemble-based randomized methods;
- Randomized methods for multi-label problems;
- Multi-view and cross-view randomized methods;
- Randomized methods for autonomous systems;
- Domain-driven solutions based on randomized methods;
- Theoretical contributions to the analysis of randomized methods;
- Applications of randomized methods for recognition, retrieval, and recommendation problems.
Dr. Jenni Raitoharju
Dr. Dimitrios Milioris
Dr. Alexandros Iosifidis
Guest Editors
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 submissions that pass pre-check are 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 1600 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.
Keywords
- Large-scale randomized pattern analysis
- Regularization
- Graph-based randomized learning
- Discriminant randomized methods
- Sparsity methods
- Matrix completion
- Randomized subspace learning
- Randomized neural networks
- Randomized hierarchical and deep learning
- Randomized unsupervised and semi-supervised learning
- Ensemble-based randomized methods
- Randomized multi-label methods
- Multi-view and cross-view randomized methods
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
