SubjectsSubjects(version: 806)
Course, academic year 2017/2018
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Matrix Computations in Statistics - NMST442
Czech title: Maticové výpočty ve statistice
Guaranteed by: Department of Numerical Mathematics (32-KNM)
Faculty: Faculty of Mathematics and Physics
Actual: from 2017
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: not taught
Language: Czech, English
Teaching methods: full-time
Guarantor: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D.
Class: M Mgr. NVM
M Mgr. NVM > Volitelné
M Mgr. PMSE
M Mgr. PMSE > Povinně volitelné
Classification: Mathematics > Numerical Analysis, Probability and Statistics
Annotation -
Last update: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D. (14.09.2013)

This course focuses on statistical methods based on matrix computations where the effective application of methods of numerical linear algebra is crucial. The main emphasis is on understanding and selecting methods that have low computational and memory requirements, and are if possible stable and reliable. The first part of the course will concentrate on statistical tasks associated with the matrix decomposition SVD, like PCA, regression, dimension reduction and the small sample size problem (especially in the case of sparse data), pattern recognition and similar classification tasks or problems from the area of data mining. In the next part we will focus on non-negative matrix decompositions used for example in text mining and on computations of numerical linear algebra that are used to solve the page ranking problem for search engines.
Literature -
Last update: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D. (29.01.2014)

ELDEN, L.: Matrix Methods in Data Mining and Pattern Recognition, Fundamentals of Algorithms, 4. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2007.

BJORCK, ÅKE: Numerical Methods for Least Squares Problems. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1996.

HIGHAM, N., STEWART, G. W.: Numerical Linear Algebra in Statistical Computing. The state of the art in numerical analysis (Birmingham, 1986), Inst. Math. Appl. Conf. Ser. New Ser., 9, Oxford Univ. Press, New York, 1987, pp. 41-57.

DUINTJER TEBBENS, J., SCHLESINGER, P.: Improving Implementation of Linear Discriminant Analysis for the High Dimension/Small Sample Size Problem, Computational Statistics and Data Analysis, 2007, vol. 52, no.1, pp. 423-437.

J. KALINA, J. DUINTJER TEBBENS: Metody pro redukci dimenze v mnohorozmerne statistice a jejich vypocet, to appear in the Informacní bulletin of the Czech Statistical Society, in 2014.

J. DUINTJER TEBBENS, I. HNETYNKOVA, M. PLESINGER, Z. STRAKOS and P. TICHY: Analysis of Methods for Matrix Computations, Basic Methods (in Czech), Matfyzpress Prague, ISBN 978-80-7378-201-6, first edition, 2012, 328 pp.

Teaching methods -
Last update: T_KPMS (12.05.2014)

Lecture+exercises.

Syllabus -
Last update: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D. (14.09.2013)

1. Numerical properties of the SVD and spectral decomposition

2. PCA and the spectral decomposition

3. (Multi)-linear regression and the SVD

4. Dimension reduction in high-dimensional statistics

5. Pattern recognition and other classification tasks

6. Nonnegative matrix decompositions

7. The page ranking problem

 
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