Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction

  • Sumet Mehta School of Computer Science and Communication Engineering,Jiangsu University, Zhenjiang, 212013, China
Keywords: manifold learning, multi-view dimension reduction, graph embedding, pattern recognition


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.

How to Cite
Sumet Mehta. (2020). Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction. Lahore Garrison University Research Journal of Computer Science and Information Technology, 4(4), 55-72.