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Original title:
Robust Knowledge Discovery from High-Dimensional Data
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /6./, Prague (CZ), 2012-09-13 / 2012-09-15
Year: 2012
Language: eng
Abstract: The paper is devoted to advanced robust methods for information extraction from highdimensional data. The concept of knowledge discovery is discussed together with its two important aspects: high dimensionality of the data and sensitivity to the presence of outlying data values. We propose new robust methods for knowledge discovery suitable for highdimensional data. They are based on the idea of implicit weighting, which is inspired by the least weighted squares regression estimator. We propose a highly robust method for a dimension reduction, which can be described as a robust alternative of the principal component analysis based on implicit down-weighting of less reliable data values. Further, we propose a novel robust approach to cluster analysis, which is a popular knowledge discovery method of unsupervised learning. A two-stage cluster analysis method tailor-made for highdimensional data is obtained by combining the robust principal component analysis with the robust cluster analysis. The procedure can be interpreted as a robust knowledge discovery method tailor made for high-dimensional data.
Keywords: cluster analysis; dimension reduction; principal components; robust statistics
Host item entry: International collection of scientific work on the occasion of 60th anniversary of university education at faculty of Business Economy with seat in Košice of University of Economics in Bratislava, ISBN 978-80-86175-80-5
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0218503
Permalink: http://www.nusl.cz/ntk/nusl-151631
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Authors: Kalina, Jan
Document type: Papers
Conference/Event: International Days of Statistics and Economics /6./, Prague (CZ), 2012-09-13 / 2012-09-15
Year: 2012
Language: eng
Abstract: The paper is devoted to advanced robust methods for information extraction from highdimensional data. The concept of knowledge discovery is discussed together with its two important aspects: high dimensionality of the data and sensitivity to the presence of outlying data values. We propose new robust methods for knowledge discovery suitable for highdimensional data. They are based on the idea of implicit weighting, which is inspired by the least weighted squares regression estimator. We propose a highly robust method for a dimension reduction, which can be described as a robust alternative of the principal component analysis based on implicit down-weighting of less reliable data values. Further, we propose a novel robust approach to cluster analysis, which is a popular knowledge discovery method of unsupervised learning. A two-stage cluster analysis method tailor-made for highdimensional data is obtained by combining the robust principal component analysis with the robust cluster analysis. The procedure can be interpreted as a robust knowledge discovery method tailor made for high-dimensional data.
Keywords: cluster analysis; dimension reduction; principal components; robust statistics
Host item entry: International collection of scientific work on the occasion of 60th anniversary of university education at faculty of Business Economy with seat in Košice of University of Economics in Bratislava, ISBN 978-80-86175-80-5
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: http://hdl.handle.net/11104/0218503
Permalink: http://www.nusl.cz/ntk/nusl-151631
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers
Record created 2013-03-13, last modified 2023-12-06