Efficient Multiple Kernel Classification Using Feature and Decision Level Fusion

摘要

Kernel methods for classification is a well-studied area in which data are implicitly mapped from a lower-dimensional space to a higher dimensional space to improve classification accuracy. However, for most kernel methods, one must still choose a kernel to use for the problem. Since there is, in general, no way of knowing which kernel is the best, multiple kernel learning (MKL) is a technique used to learn the aggregation of a set of valid kernels into a single (ideally) superior kernel. The aggregation can be done using weighted sums of the precomputed kernels, but determining the summation weights is not a trivial task. Furthermore, MKL does not work well with large datasets because of limited storage space and prediction speed. In this paper, we address all three of these multiple kernel challenges. First, we introduce a new linear feature level fusion technique and learning algorithm, GAMKLp. Second, we put forth three new algorithms, DeFIMKL, DeGAMKL, and DeLSMKL, for nonlinear fusion of kernels at the decision level. To address MKL's storage and speed drawbacks, we apply the Nystrom approximation to the kernel matrices. We compare our methods to a successful and state-of-the-art technique called MKL-group lasso (MKLGL), and experiments on several benchmark datasets show that some of our proposed algorithms outperform MKLGL when applied to support vector machine (SVM)-based classification. However, to no surprise, there does not seem to be a global winner but instead different strategies that a user can employ. Experiments with our kernel approximation method show that we can routinely discard most of the training data and at least double prediction speed without sacrificing classification accuracy. These results suggest that MKL-based classification techniques can be applied to big data efficiently, which is confirmed by an experiment using a large dataset.