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Recently, manifold regularized semi-supervised learning (MRSSL) receivedconsiderable attention because it successfully exploits the geometry of theintrinsic data probability distribution including both labeled and unlabeledsamples to leverage the performance of a learning model. As a natural nonlineargeneralization of graph Laplacian, p-Laplacian has been proved having the richtheoretical foundations to better preserve the local structure. However, it isdifficult to determine the fitting graph p-Lapalcian i.e. the parameter whichis a critical factor for the performance of graph p-Laplacian. Therefore, wedevelop an ensemble p-Laplacian regularization (EpLapR) to fully approximatethe intrinsic manifold of the data distribution. EpLapR incorporates multiplegraphs into a regularization term in order to sufficiently explore thecomplementation of graph p-Laplacian. Specifically, we construct a fused graphby introducing an optimization approach to assign suitable weights on differentp-value graphs. And then, we conduct semi-supervised learning framework on thefused graph. Extensive experiments on UC-Merced data set demonstrate theeffectiveness and efficiency of the proposed method.

Ensemble p-Laplacian Regularization for Remote Sensing Image Recognition