Multiscale laplacian learning

Ekaterina Merkurjev, Duc Duy Nguyen, Guo Wei Wei

Research output: Contribution to journalArticlepeer-review


Machine learning has greatly influenced a variety of fields, including science. However, despite tremendous accomplishments of machine learning, one of the key limitations of most existing machine learning approaches is their reliance on large labeled sets, and thus, data with limited labeled samples remains an important challenge. Moreover, the performance of machine learning methods is often severely hindered in case of diverse data, which is usually associated with smaller data sets or data associated with areas of study where the size of the data sets is constrained by high experimental cost and/or ethics. These challenges call for innovative strategies for dealing with these types of data. In this work, the aforementioned challenges are addressed by integrating graph-based frameworks, semi-supervised techniques, multiscale structures, and modified and adapted optimization procedures. This results in two innovative multiscale Laplacian learning (MLL) approaches for machine learning tasks, such as data classification, and for tackling data with limited samples, diverse data, and small data sets. The first approach, multikernel manifold learning (MML), integrates manifold learning with multikernel information and incorporates a warped kernel regularizer using multiscale graph Laplacians. The second approach, the multiscale MBO (MMBO) method, introduces multiscale Laplacians to the modification of the famous classical Merriman-Bence-Osher (MBO) scheme, and makes use of fast solvers. We demonstrate the performance of our algorithms experimentally on a variety of benchmark data sets, and compare them favorably to the state-of-art approaches.

Original languageEnglish
Pages (from-to)15727-15746
Number of pages20
JournalApplied Intelligence
Issue number12
StatePublished - Jun 2023

Bibliographical note

Funding Information:
This work was supported in part by NSF Grants DMS-2052983, DMS-2053284, DMS2151802, DMS-1761320, and IIS1900473, NIH grants R01GM126189 and NIH R01AI164266, Bristol-Myers Squibb, Pfizer, MSU Foundation, and University of Kentucky Startup Fund.

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Graph laplacian
  • Graph-based methods
  • Manifold learning
  • Multiscale framework

ASJC Scopus subject areas

  • Artificial Intelligence


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