Advanced linear and nonlinear numerical methods in data analysis

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Advanced linear and nonlinear numerical methods in data analysis

Code: 239828
ECTS: 5.0
Lecturers in charge: prof. dr. sc. Zlatko Drmač
Take exam: Studomat
Load:

1. komponenta

Lecture typeTotal
Lectures 45
* Load is given in academic hour (1 academic hour = 45 minutes)
Description:
Literature:
  1. TensorTrain decomposition, I. Oseledets, SIAM J. Sci. Comput. Vol. 33, No. 5, 2011.
  2. Tensor-Train decomposition for image recognition, D. Brandoni, V. Simoncini, HAL-02196526, 2019.
  3. Diffusion maps, spectral clustering and reaction coordinates of dynamical systems, B. Nadler, S. Lafon, R. R, Coifman, I. G. Kevrekidis, Appl. Comput. Harmonic Analysis 21, 2006.
  4. Matrix Methods in Data Mining and Pattern Recognition, Lars Elden, SIAM, 2007.
  5. Handwritten digit classification using higher order singular value decomposition, B. Savas, L. Elden, Pattern Recognition 40(3), 2007.
  6. Diffusion maps, R. R. Coifman, S. Lafon, Appl. Comput. Harmonic Analysis 21, 2006.
Prerequisit for:
Enrollment :
Attended : Matrix and tensor methods in data analysis

Examination :
Passed : Matrix and tensor methods in data analysis
1. semester Not active
Izborni predmet 1, 2 - Regular study - Computer Science and Mathematics

2. semester
Izborni predmet 1, 2 - Regular study - Computer Science and Mathematics

3. semester Not active
Izborni predmet 3, 4, 5, 6 - Regular study - Computer Science and Mathematics

4. semester
Izborni predmet 3, 4, 5, 6 - Regular study - Computer Science and Mathematics
Consultations schedule: