A Unified Neighbor Reconstruction Method for Embeddings

In this work we propose a novel and compact Neighbor Reconstruction Method (NRM) which is a unified pre-processing method for graph-based sparse spectral algorithms. This method is conducted by vector operations on a central point and its corresponding neighbor points. NRM generates new neighbor points which can capture the local space structure of the central point more appropriately than original neighbor points. With NRM, a large number of sparse spectral based nonlinear feature extraction and selection algorithms gain significant improvement. Specifically, we embedded NRM to several classical algorithms, Local Linear Embedding (LLE) [1], Laplacian Eigenmaps (LE) [2] and Unsupervised Feature Selection for Multi-cluster Data (MCFS) [3], with accuracy improvement of up to 7%, 2.6%, 2.4% on ORL, CIFAR 10, and MINST data sets respectively. We also apply NRM to a Super Resolution algorithm, A+ [5], and obtain 0.12dB improvement than original method.