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Proceedings Paper

Extraction of essential features by quantum density
Author(s): Artur Wilinski
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Paper Abstract

In this paper we consider the problem of feature extraction, as an essential and important search of dataset. This problem describe the real ownership of the signals and images. Searches features are often difficult to identify because of data complexity and their redundancy. Here is shown a method of finding an essential features groups, according to the defined issues. To find the hidden attributes we use a special algorithm DQAL with the quantum density for thej-th features from original data, that indicates the important set of attributes. Finally, they have been generated small sets of attributes for subsets with different properties of features. They can be used to the construction of a small set of essential features. All figures were made in Matlab6.

Paper Details

Date Published: 28 September 2016
PDF: 7 pages
Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 100315C (28 September 2016); doi: 10.1117/12.2249406
Show Author Affiliations
Artur Wilinski, Warsaw Univ. of Life Sciences (Poland)


Published in SPIE Proceedings Vol. 10031:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016
Ryszard S. Romaniuk, Editor(s)

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