Manifold Learning and Dimensionality Reduction

Dear Colleagues,

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.

Stephan Chalup
Guest Editor

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