Laplacian eigenmaps based manifold regularized CNN for visual

The paper proposes a novel Laplacian eigenmaps based manifold regularized CNN (LE-CNN) for action recognition. The proposed LE-CNN model incorporates Laplacian eigenmaps based manifold structure information of training samples into CNN layer by layer, which can keep adjacent samples as close as possible during space transformation. In addition, the Laplacian eigenmaps based manifold structure can accelerate convergence during the training process. Experiments are performed on two standard action datasets, UCF101 and HMDB51, to validate the proposed LE-CNN model. Furthermore, we extend experiments on a large-scale action recognition dataset (i.e. the Kinetics dataset) and compare the LE-CNN model with other advanced models. In addition to action recognition, we also apply the LE-CNN model to an image classification task on the CIFAR-10 dataset, to demonstrate the effectiveness of the LE-CNN model across different classification tasks.