Aiming at the classification performance deficiencies of current supervised learning algorithms on manifold data sets

e.g. low classification accuracy and limited sparsity

a sparse manifold learning algorithm based on sparse Bayesian inference and manifold regularization framework is proposed. The algorithm is called manifold learning based on sparse Bayesian approach (MLSBA). MLSBA is an extension of sparse Bayesian model

by introducing sparse manifold priors to the weights

which can effectively employ the manifold information of sample data to improve the classification accuracy. Extensive experiments are conducted on various datasets

and the results show that MLSBA not only achieves better classification performance on manifold datasets

but also has comparable effectiveness on the non-manifold datasets

and our algorithm has good sparsity on two categories of datasets at the same time.