IMPRS Seminar - Tandem Talk with Kirandeep Kour and Prof. Dr. Peter Benner

IMPRS Seminars - Tandem Talk: "Tensor methods for Machine Learning"

  • Date: Jan 13, 2023
  • Time: 02:00 PM - 03:30 PM (Local Time Germany)
  • Speaker: Kirandeep Kour, Prof. Dr. Peter Benner
  • Location: Magdeburg
  • Room: Online
  • Host: IMPRS
IMPRS Seminar - Tandem Talk with Kirandeep Kour and Prof. Dr. Peter Benner

The next IMPRS Seminar will be held by Prof. Dr. Peter Benner and Kirandeep Kour

They will speak about "Low -rank Tensor Decompositions in Kernel-based Machine Learning". The talk will be held hybrid. It is possible to attend online as well as in person.

Tandem talks at IMPRS seminars are part of the IMPRS program and an excellent opportunity for researchers to exchange current findings and challenges. If you want to take part in the talks, please write a mail to imprs@mpi-magdeburg.mpg.de for login details.

Everyone interested is invited to the talks.

Abstract:
Enormous amounts of data are being generated, in several scientific

applications, e.g., neuroscience, medical science, and signal processing.

Generally, these data depend on various parameters and thus can be in-

terpreted as multidimensional data (tensor) and incorporate structure in

the multi-dimensionality. Working with tensor datasets usually requires

high computational cost due to the curse of dimensionality. The Low-rank

tensor decompositions (LRTD) reduce the aforementioned issue and aim

at providing a natural and compact structure-preserving representation.

In the doctoral project, we have worked on the development of LRTD

algorithms for binary classification with small sample-sized datasets like

functional Magnetic Resonance Imaging (fMRI), CT scans, and Hyper-

spectral Images. The kernel computation (feature extraction technique)

in the Kernel-based method such as Support Tensor Machine (extended

version of Support Vector Machine for tensor data) benefits from newly

established LRTD methods. The benefits include achieving a robust, com-

putationally efficient machine learning model along with state-of-the-art

classification accuracy.

The talk will include an introductory session, followed by a detailed

description of the work with some numerical experiments and results.

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