Manifold Modeling in Machine Learning

Research output: Contribution to journalArticlepeer-review

Abstract

Abstract: Predictive Modeling problems deal with high-dimensional data; however, the curse of dimensionality presents an obstacle to the use of many methods for their solutions. In many applications, real-world data occupy only a very small part of high-dimensional observation space whose intrinsic dimension is essentially lower than that of the space. A popular model for such data is the manifold model, according to which the data lie on an unknown low-dimensional manifold (Data Manifold) embedded in the ambient high-dimensional space. Predictive modeling problems, which are studied under this assumption, are called manifold estimation problems. The general goal of such problems is to identify the low-dimensional structure of multidimensional data from a given dataset. If dataset points are sampled according to an unknown probability measure on the data manifold, there is a need to model manifolds when solving various machine learning problems. We provide a short survey of such problems and outline some approaches to their solution.

Original languageEnglish
Pages (from-to)754-763
Number of pages10
JournalJournal of Communications Technology and Electronics
Volume66
Issue number6
DOIs
Publication statusPublished - Jun 2021

Keywords

  • dimension reduction
  • manifold modeling
  • predictive modeling

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