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Special Issue "Manifold Learning and Dimensionality Reduction"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 November 2015) | Viewed by 11912
Special Issue Editor
Interests: manifold learning and kernel methods; sequences and time series; reinforcement learning and neural information processing
Special Issue Information
While computers have become faster and memory has become more affordable, new algorithmic challenges have arrived with the desire to analyze large and high-dimensional datasets. A central question is how to trade off the efficiency of computation against precision in data analytics. This Special Issue addresses machine learning, pattern recognition, and data analysis techniques and the applications that are related to dimensionality reduction. In this context, there is the question of whether techniques of non-linear dimensionality reduction can be fast enough to process big datasets in a reasonable time, and possibly on low-powered devices. There is also the hope that the ability to process large enough datasets will allow suitable manifold learning approaches to extract manifolds that otherwise would collapse. This Special Issue will consider applied, experimental, and theoretical work that can help shed light on this topic domain, including related work on optimization or machine learning on manifolds.
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.
- Big Data Analytics
- Deep Learning
- Dimensionality Reduction
- Kernel Machines
- Large or Sequence Data Processing
- Manifold Learning
- Non-linear Pattern Analysis
- Optimization on Manifolds