Fuzzy soft information measures and their applications in dimension reduction and pattern recognition

2021 ◽  
Author(s):  
Seema Singh ◽  
D. S. Hooda ◽  
S. C. Malik
Keyword(s):  
Pattern Recognition ◽  
Dimension Reduction ◽  
Soft Information ◽  
Information Measures

Dimension Reduction Methods for Image Pattern Recognition

2013 ◽  
pp. 26-42 ◽  
Author(s):  
Hayato Itoh ◽  
Tomoya Sakai ◽  
Kazuhiko Kawamoto ◽  
Atsushi Imiya
Keyword(s):  
Pattern Recognition ◽  
Dimension Reduction ◽  
Reduction Methods ◽  
Image Pattern

scholarly journals Topology-Preserving Dimension-Reduction Methods for Image Pattern Recognition

2013 ◽  
pp. 195-204 ◽  
Author(s):  
Hayato Itoh ◽  
Tomoya Sakai ◽  
Kazuhiko Kawamoto ◽  
Atsushi Imiya
Keyword(s):  
Pattern Recognition ◽  
Dimension Reduction ◽  
Reduction Methods ◽  
Image Pattern

Quantum Dimension Reduction for Pattern Recognition in High-Resolution Spatio-Spectral Data

2020 ◽  
pp. 1-1
Author(s):  
Naveed Mahmud ◽  
Bennett Haase-Divine ◽  
Andrew MacGillivray ◽  
Esam El-Araby
Keyword(s):  
Pattern Recognition ◽  
High Resolution ◽  
Dimension Reduction ◽  
Spectral Data

A Dimension Reduction Method and its Application in GIS Partial Discharge Pattern Recognition Research

2014 ◽  
Vol 602-605 ◽  
pp. 2105-2109
Author(s):  
Fang Cheng Lv ◽  
Hu Jin ◽  
Zi Jian Wang ◽  
Bo Zhang
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Dimension Reduction ◽  
Partial Discharge ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Discharge Pattern ◽  
Insulation Defect ◽  
Mean Vector

GIS partial discharge pattern recognition is an important part of its state evaluation, authors have set up 252kV GIS partial discharge detection simulation experiment platform based on UHF detection method, and designed four kinds of typical partial discharge models in laboratory, then established corresponding UHF signal mapping database through the experimental method, and also extracted the original feature parameters; because the original characteristic dimension is high, which is bad for pattern recognition, based on this, the article uses a species mean kernel principal component analysis method, it mapped the partial discharge original data samples to high-dimensional feature space, at first, it calculate all kinds of class mean vector data, and then do principal component analysis based on class mean vector space, build the class average kernel matrix, at last, the class kernel mean principal component analysis algorithm is established. Results show that characteristic of this method contained all the information of the original data, and dimension is less than GIS insulation defect category numbers, and it can realize data dimension reduction without information loss, which improve the pattern recognition rate.

scholarly journals New similarity measures for single-valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems

2020 ◽  
Author(s):  
Jia Syuen Chai ◽  
Ganeshsree Selvachandran ◽  
Florentin Smarandache ◽  
Vassilis C. Gerogiannis ◽  
Le Hoang Son ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Medical Diagnosis ◽  
Similarity Measures ◽  
Rank Correlation ◽  
Distance Measures ◽  
Neutrosophic Set ◽  
Neutrosophic Sets ◽  
Numerical Examples ◽  
Information Measures ◽  
Comparison Results

AbstractThe single-valued neutrosophic set (SVNS) is a well-known model for handling uncertain and indeterminate information. Information measures such as distance measures, similarity measures and entropy measures are very useful tools to be used in many applications such as multi-criteria decision making (MCDM), medical diagnosis, pattern recognition and clustering problems. A lot of such information measures have been proposed for the SVNS model. However, many of these measures have inherent problems that prevent them from producing reasonable or consistent results to the decision makers. In this paper, we propose several new distance and similarity measures for the SVNS model. The proposed measures have been verified and proven to comply with the axiomatic definition of the distance and similarity measure for the SVNS model. A detailed and comprehensive comparative analysis between the proposed similarity measures and other well-known existing similarity measures has been done. Based on the comparison results, it is clearly proven that the proposed similarity measures are able to overcome the shortcomings that are inherent in existing similarity measures. Finally, an extensive set of numerical examples, related to pattern recognition and medical diagnosis, is given to demonstrate the practical applicability of the proposed similarity measures. In all numerical examples, it is proven that the proposed similarity measures are able to produce accurate and reasonable results. To further verify the superiority of the suggested similarity measures, the Spearman’s rank correlation coefficient test is performed on the ranking results that were obtained from the numerical examples, and it was again proven that the proposed similarity measures produced the most consistent ranking results compared to other existing similarity measures.

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