Feature selection becomes a prominent method in the big data era. The logistic regression model is a wrapper method that provides better classification or prediction accuracy but it is computationally expensive. In this study, we propose the random subspace logistic regression where features are randomly selected through bootstrap cycles. The random subspace regression method is applied to both standard and lasso logistic regression models. Using the simulated and empirical data, our proposed random subspace logistic regression shows favorable results and can be a promising alternative for flat feature selection.

Network embedding has been intensively studied in the literature and widely used in various applications, such as link prediction and node classification. While previous work focus on the design of new algorithms or are tailored for various problem settings, the discussion of initialization strategies in the learning process is often missed. In this work, we address this important issue of initialization for network embedding that could dramatically improve the performance of the algorithms on both effectiveness and efficiency. Specifically, we first exploit the graph partition technique that divides the graph into several disjoint subsets, and then construct an abstract graph based on the partitions. We obtain the initialization of the embedding for each node in the graph by computing the network embedding on the abstract graph, which is much smaller than the input graph, and then propagating the embedding among the nodes in the input graph. With extensive experiments on various datasets, we demonstrate that our initialization technique significantly improves the performance of the state-of-the-art algorithms on the evaluations of link prediction and node classification by up to 7.76% and 8.74% respectively. Besides, we show that the technique of initialization reduces the running time of the state-of-the-arts by at least 20%.