Rough Entropy-Based Fused Granular Features in 2-D Locality Preserving Projections for High-Dimensional Vision Sensor Data

Locality preserving projection (LPP) is a manifold learning-based nonlinear dimensionality reduction (DR) technique, which has seen successful implementations in pattern recognition problems. In the case of 2-D images, if they are vectorized in 1-D shapes before applying LPP, significant spatial neighborhood information can be lost. Two-dimensional LPP (2DLPP) can overcome this problem but suffers when data are susceptible to noise, outliers, and intensity variation. To address these issues, we propose a novel rough entropy-based granular fusion (REGF) scheme to capture the intensity variation in the data in the form of feature information and propose hybridization of REGF with 2DLPP for feature extraction. The fusion technique simultaneously imbibes good features of crisp granulation (CG) and quad-tree decomposition (QTD), and considers the uncertainties caused by these homogenous and non-homogeneous granulation techniques in defining the indiscernible image regions. Moreover, it works in the RGB color space to alleviate the loss of information encountered by the conventional granulation techniques in the gray space. Extensive experimental studies in a real-world vision sensor-based human–robot interaction (HRI) framework have been conducted to demonstrate the effectiveness of the proposed granular computing (GrC)-based technique, especially in rugged environments.