  Recently, manifold regularized semi-supervised learning (MRSSL) received
considerable attention because it successfully exploits the geometry of the
intrinsic data probability distribution including both labeled and unlabeled
samples to leverage the performance of a learning model. As a natural nonlinear
generalization of graph Laplacian, p-Laplacian has been proved having the rich
theoretical foundations to better preserve the local structure. However, it is
difficult to determine the fitting graph p-Lapalcian i.e. the parameter which
is a critical factor for the performance of graph p-Laplacian. Therefore, we
develop an ensemble p-Laplacian regularization (EpLapR) to fully approximate
the intrinsic manifold of the data distribution. EpLapR incorporates multiple
graphs into a regularization term in order to sufficiently explore the
complementation of graph p-Laplacian. Specifically, we construct a fused graph
by introducing an optimization approach to assign suitable weights on different
p-value graphs. And then, we conduct semi-supervised learning framework on the
fused graph. Extensive experiments on UC-Merced data set demonstrate the
effectiveness and efficiency of the proposed method.
