Paper Info
Title
Robust Fuzzy Rough Set Based Dimensionality Reduction For Big Multimedia Data Hashing And Unsupervised Generative Learning
Abstract
The amount of high dimensional data produced by visual sensors in the smart environments and by autonomous vehicles is increasing exponentially. In order to search and model this data for real-time applications, the dimensionality of the data should be reduced. In this paper, a novel dimensionality reduction algorithm based on fuzzy rough set theory, called Centralized Binary Mapping (CBM), is proposed. The fuzzy CBM kernel is used for extracting the central elements and the memory cells from the blocks of high dimensional data. The proposed applications of CBM in this paper include hashing and generative modelling of multimedia big data. The robustness of the proposed CBM based hashing algorithm is 10% higher than comparable methods. Furthermore, based on the CBM, a novel architecture for neural networks called Deep Root Dimensional Mapping (DRDM) is proposed. The DRDM is used for generative modelling of multimedia big data using a new autonomous vehicle visual navigation dataset as well as the standard datasets. The simulation results show that the proposed DRDM converges rapidly and the perceptual quality of the outputs at the same epoch is higher than generative adversarial networks. The proposed CBM can be used as a new data structures in various pattern recognition and machine learning tasks.
Year
DOI
Venue
2021
10.1007/s11042-021-10571-2
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
Fuzzy rough set, Multimedia big data, Image hashing, Neural networks, Generative learning, Pattern recognition
Journal
80
Issue
ISSN
Citations 
12
1380-7501
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Pouria Khanzadi100.68
Babak Majidi2112.91
Sepideh Adabi3324.97
Jagdish C. Patra400.34
A. Movaghar519732.28