Open AccessGeneralized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction
Author(s) -
Sumet Mehta
Publication year - 2020
Publication title -
lahore garrison university research journal of computer science and information technology
Language(s) - English
Resource type - Journals
eISSN - 2521-0122
pISSN - 2519-7991
DOI - 10.54692/lgurjcsit.2020.0404109
Subject(s) - dimensionality reduction , adjacency list , nonlinear dimensionality reduction , adjacency matrix , embedding , graph , computer science , locality , intrinsic dimension , manifold (fluid mechanics) , curse of dimensionality , graph embedding , mathematics , theoretical computer science , algorithm , artificial intelligence , mechanical engineering , linguistics , philosophy , engineering
In this paper, we propose a new dimension reduction (DR) algorithm called ensemble graph-based locality preserving projections (EGLPP); to overcome the neighborhood size k sensitivity in locally preserving projections (LPP). EGLPP constructs a homogeneous ensemble of adjacency graphs by varying neighborhood size k and finally uses the integrated embedded graph to optimize the low-dimensional projections. Furthermore, to appropriately handle the intrinsic geometrical structure of the multi-view data and overcome the dimensionality curse, we propose a generalized multi-manifold graph ensemble embedding framework (MLGEE). MLGEE aims to utilize multi-manifold graphs for the adjacency estimation with automatically weight each manifold to derive the integrated heterogeneous graph. Experimental results on various computer vision databases verify the effectiveness of proposed EGLPP and MLGEE over existing comparative DR methods.