Robust fuzzy rough set based dimensionality reduction for big multimedia data hashing and unsupervised generative learning

摘要

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.