Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
7-24-2016
Abstract
Fundamental challenges and goals of the cognitive algorithms are moving super-intelligent machines and super-intelligent humans from dreams to reality. This paper is devoted to a technical way to reach some specific aspects of super-intelligence that are beyond the current human cognitive abilities. Specifically the proposed technique is to overcome inabilities to analyze a large amount of abstract numeric high-dimensional data and finding complex patterns in these data with a naked eye. Discovering patterns in multidimensional data using visual means is a long-standing problem in multiple fields and Data Science and Modeling in general. The major challenge is that we cannot see n-D data by a naked eye and need visualization tools to represent n-D data in 2-D losslessly. The number of available lossless methods is quite limited. The objective of this paper is expanding the class of such lossless methods, by proposing a new concept of Generalized Shifted Collocated Paired Coordinates. The paper shows the advantages of proposed lossless technique by proving mathematical properties and by demonstration on real data.
Recommended Citation
Kovalerchuk, Boris, "Super-intelligence Challenges and Lossless Visual Representation of High-Dimensional Data" (2016). All Faculty Scholarship for the College of the Sciences. 224.
https://digitalcommons.cwu.edu/cotsfac/224
Journal
2016 International Joint Conference on Neural Networks (IJCNN)
Rights
© 2016 IEEE
Comments
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