Inference and Prediction in Big Data Using Sparse Gaussian Process Method

Gaussian process is one of computationally expensive algorithm for large datasets and lack of the flexibility to model different datasets is a common problem for modeling it. We introduce sparse Gaussian regression with the combination of designed kernels to solve the computational complexity of a traditional Gaussian process by taking pseudo input from large datasets and developing a model with better accuracy which enables Gaussian process application. We design a better combination of the kernel that can catch up with most of our data points. We demonstrate the approach on a large weather dataset and sales record dataset. Both are open source big datasets available online. Numerous experiments and comparisons with traditional Gaussian process methods using both large datasets demonstrate the efficiency and accuracy of sparse Gaussian processes.