Deep convolutional neural networks show great advantages in computer vision tasks, such as image classification and object detection. However, the networks have complex network structure which include a large number of layers such as convolutional layers and pooling layers. They greatly consume valuable computing and memory resources, and also hugely waste training time. Therefore, we propose a novel shallow convolutional neural network (SCNNB) to overcome the above limitations for image classification, which uses batch normalization techniques to accelerate training convergence and improve the accuracy. The SCNNB network has only 4 layers with small size of convolution kernels, which requires low time complexity and space complexity. In the experiments, we compare the SCNNB model with two variant models and the classical SCNN model on the two benchmark image datasets. Experimental results show that compared to SCNN model, the SCNNB model can quickly learn the features of the data and achieve the highest classification accuracy of 93.69% with 3.8 M time complexity on fashion-MNIST.

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Graph convolutional network (GCN) is an efficient network for learning graph representations. However, it costs expensive to learn the high-order interaction relationships of the node neighbor. In this paper, we propose a novel graph convolutional model to learn and fuse multihop neighbor information relationships. We adopt the weight-sharing mechanism to design different order graph convolutions for avoiding the potential concerns of overfitting. Moreover, we design a new multihop neighbor information fusion (MIF) operator which mixes different neighbor features from 1-hop to k-hops. We theoretically analyse the computational complexity and the number of trainable parameters of our models. Experiment on text networks shows that the proposed models achieve state-of-the-art performance than the text GCN.

A number of literature reports have shown that multi-view clustering can acquire a better performance on complete multi-view data. However, real-world data usually suffers from missing some samples in each view and has a small number of labeled samples. Additionally, almost all existing multi-view clustering models do not execute incomplete multi-view data well and fail to fully utilize the labeled samples to reduce computational complexity, which precludes them from practical application. In view of these problems, this paper proposes a novel framework called Semi-supervised Multi-View Clustering with Weighted Anchor Graph Embedding (SMVC_WAGE), which is conceptually simple and efficiently generates high-quality clustering results in practice. Specifically, we introduce a simple and effective anchor strategy. Based on selected anchor points, we can exploit the intrinsic and extrinsic view information to bridge all samples and capture more reliable nonlinear relations, which greatly enhances efficiency and improves stableness. Meanwhile, we construct the global fused graph compatibly across multiple views via a parameter-free graph fusion mechanism which directly coalesces the view-wise graphs. To this end, the proposed method can not only deal with complete multi-view clustering well but also be easily extended to incomplete multi-view cases. Experimental results clearly show that our algorithm surpasses some state-of-the-art competitors in clustering ability and time cost.

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the computational complexity, we propose a novel information fusion pooling layer to combine the high-order and low-order neighborhood matrix information. We theoretically compare the computational complexity and the number of parameters of the proposed model with those of the other state-of-the-art models. Experimentally, we verify the proposed model on large-scale text network datasets using supervised learning and on citation network datasets using semisupervised learning. The experimental results show that the proposed model achieves higher classification accuracy with a small set of trainable weight parameters.