The extension of the largest generalized-eigenvalue based distance metric ) in arbitrary feature spaces to classify composite data points

The extension of the largest generalized-eigenvalue based distance metric ) in arbitrary feature spaces to classify composite data points

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogen...

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Bibliographic Details
Main Author: Mosaab Daoud
Format: Article
Language:English
Published: Korea Genome Organization 2019-11-01
Series:Genomics & Informatics
Subjects:
classification
clustering
composite data points
limiting dispersion map
linear (non-linear) transformation function
sets of sequences
statistical learning
Online Access:http://genominfo.org/upload/pdf/gi-2019-17-4-e39.pdf

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http://genominfo.org/upload/pdf/gi-2019-17-4-e39.pdf

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