Learning Graph Similarity With Large Spectral Gap

An Integrated Cluster Detection, Optimization, and Interpretation Approach for Financial Data

In many financial applications, such as fraud detection, reject inference, and credit evaluation, detecting clusters automatically is critical because it helps to understand the subpatterns of the data that can be used to infer user's behaviors and identify potential risks. Due to the complexity of human behaviors and changing social environments, the distributions of financial data are usually complex and it is challenging to find clusters and give reasonable interpretations. The goal of this study is to develop an integrated approach to detect clusters in financial data, and optimize the scope of the clusters such that the clusters can be easily interpreted. Specifically, we first proposed a new cluster quality evaluation criterion, which is free from large-scale computation and can guide base clustering algorithms such as k -Means to detect hyperellipsoidal clusters adaptively. Then, we designed a new solver for a revised support vector data description model, which efficiently refines the centroids and scopes of the detected clusters to make the clusters tighter such that the data in the clusters share greater similarities, and thus, the clusters can be easily interpreted with eigenvectors. Using ten financial datasets, the experiments showed that the proposed algorithm can efficiently find reasonable number of clusters. The proposed approach is suitable for large-scale financial datasets whose features are meaningful, and also applicable to financial mining tasks, such as data distribution interpretation and anomaly detection.

Deep Learning-Based Predictive Control of Injection Velocity in Injection Molding Machines

Rapid and reliable optimal control of injection molding machines (IMMs) is critical for the effective production of injection-molded goods, especially in the situation of restricted computer resources of embedded equipment in IMMs. In this paper, an optimal tracking injection velocity control problem arising in a typical IMM is studied. An effective hybrid intelligent control approach with less computing resources for real-time implementation based on the deep learning (DL) method to mimic the classical model predictive control rule is developed to deal with the tracking control of the injection speed. The proposed method utilizes the gated recurrent unit neural network to learn and predict the optimal time series control process data produced by the traditional model predictive controller. The benefits of this approach over the conventional optimization method are illustrated through simulation results, which show that the convergent DL-based controller can effectively avoid the complex calculation in the control process of IMMs and meet the requirements of more robustness and resist environmental uncertainty to a certain level and can be potentially implemented in embedded hardware much more efficiently and conveniently with a smaller memory footprint and faster computation time.