NEWS    Andrew Knyazev (MERL) presents at the Schlumberger-Tufts U. Computational and Applied Math Seminar

Date:

April 10, 2018

Description:

Andrew Knyazev, Distinguished Research Scientist of MERL, has accepted an invitation to speak about his work on Big Data and spectral graph partitioning at the Schlumberger-Tufts U. Computational and Applied Math Seminar. A primary focus of this seminar series is on mathematical and computational aspects of remote sensing. A partial list of the topics of interest includes: numerical solution of large scale PDEs (a.k.a. forward problems); theory and numerical methods of inverse and ill-posed problems; imaging; related problems in numerical linear algebra, approximation theory, optimization and model reduction. The seminar meets on average once a month, the location alternates between Schlumberger's office in Cambridge, MA and the Tufts Medford Campus.

Abstract: Data clustering via spectral graph partitioning requires constructing the graph Laplacian and solving the corresponding eigenvalue problem. We consider and motivate using negative edge weights in the graph Laplacian. Preconditioned iterative solvers for the Laplacian eigenvalue problem are discussed and preliminary numerical results are presented.

Research Area:

Machine Learning

• Related Publications

   Zhuzhunashvili, D., Knyazev, A., "Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge", IEEE HPEC Graph Challenge, DOI: 10.1109/​HPEC.2017.8091045, September 2017, pp. 1-6.

  BibTeX TR2017-131 PDF

  • @inproceedings{Zhuzhunashvili2017sep,
  • author = {Zhuzhunashvili, David and Knyazev, Andrew},
  • title = {Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge},
  • booktitle = {IEEE HPEC Graph Challenge},
  • year = 2017,
  • pages = {1--6},
  • month = sep,
  • doi = {10.1109/HPEC.2017.8091045},
  • url = {https://www.merl.com/publications/TR2017-131}
  • }

   Knyazev, A., "Signed Laplacian for Spectral Clustering Revisited", arXiv, January 2017.

  BibTeX arXiv

  • @article{Knyazev2017jan,
  • author = {Knyazev, Andrew},
  • title = {Signed Laplacian for Spectral Clustering Revisited},
  • journal = {arXiv},
  • year = 2017,
  • month = jan,
  • url = {https://arxiv.org/abs/1701.01394}
  • }