Special Issue "Algorithms for Manifold Learning and Its Applications"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 April 2019).
Interests: scalable anomaly detection; data mining for big graphs; temporal and spatial data
We invite you to submit your latest research in the area of manifold learning to this Special Issue, “Algorithms for Manifold Learning and Its Applications”. The progress in science and engineering depends more than ever on our ability to analyze huge amounts of sensor and simulation data. The vast majority of this data, coming from, for example, high performance high fidelity numerical simulations, high resolution scientific instruments (microscopes, DNA sequencers, etc.) or Internet of Things streams and feeds, is a result of complex non-linear processes. While these non-linear processes can be characterized by low dimensional sub-manifolds, the actual observable data they generate is high dimensional. Revealing the low-dimensional representation of such high-dimensional data sets not only leads to a more compact description of the data, but also enhances our understanding of the system. Manifold learning-based dimensionality reduction algorithms are an important class of solutions presented for this problem. Such algorithms assume that the observed data lies on a low-dimensional manifold, embedded in a high-dimensional space. Manifold-learning algorithms attempt to recover the original low-dimensional domain structure in different ways.
We are looking for new and innovative approaches for solving the problem of manifold learning, with an emphasis on handling the big data challenges encountered in real-world applications. High-quality papers are solicited that address theoretical foundations, computational and other algorithmic challenges and present novel applications. Potential topics include, but are not limited to, real-time manifold learning, handling potential dependencies in the observed data, dealing with data from multiple manifolds, and accelerating manifold learning on upcoming computational architectures.
Dr. Varun Chandola
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.
- Theoretical guarantees for convergence
- Quantitative measures for measuring quality
- Complexity issues with manifold learning
- Learning from high throughput streams
- Deployment on new architectures, e.g., mobile supercomputers
- Handling non-traditional data, images, etc.
- Big data analytics
- Novel applications