Unsupervised local linear preserving manifold reduction with uncertainty pretraining for image recognition

Abstract

Manifold learning is an efficient dimensionalilty reduction algorithm. But in real applications, difficulty lies in learning the parameters with limited supervised samples. Our proposed algorithm focuses on sparse representation of local linear preserving manifold dimensionality reduction algorithm and can solve the problem of unsupervised clustering. The manifold preserving methods take use of labeled data in manifold reduction except for the final classifier which produces unsupervised manifold reduction algorithm. Another solution for limited data is a novel proposed pretraining using Bayesian nets to construct the initial parameters for manifold learning, which is also robust to data w.r.t. uncertain perturbations. Then we show its validation in experiments and finally apply the algorithm for real world data. The algorithm performs better in noisy input with limited labeled data.