Towards Factorized SVM with Gaussian Kernels over Normalized Data

There is an emerging trend of integrating machine learning (ML) techniques into database systems (DB). Considering that almost all the ML toolkits assume that the input of ML algorithms is a single table even though many real-world datasets are stored as multiple tables due to normalization in DB. Thus, data scientists have to perform joins before learning a ML model. This strategy is called learning after joins, which incurs redundancy avoided by normalization. In the area of ML, the Support Vector Machine (SVM) is one of the most standard classification tools. In this paper, we focus on the factorized SVM with gaussian kernels over normalized data. We present factorized learning approaches for two main SVM optimization methods, i.e., Gradient Descent (GD) and Sequential Minimal Optimization (SMO), by factorizing gaussian kernel function computation. Furthermore, we transform the normalized data into matrices, and boost the efficiency of SVM learning via linear algebra operations. Extensive experiments with nine real normalized data sets demonstrate the efficiency and scalability of our proposed approaches.