Appendix for the Paper: Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
نویسندگان
چکیده
To assist later deriviations and avoid reiterating the material in the main paper, we briefly present important equations for Sparse Spectrum GPs (SSGPs). Based on Bochner’s theorem, continuous shift-invariant kernels can be unbiasedly approximated by an explicit finitedimensional feature map. Leveraging this approximation, we consider SSGPs which is a class of Gaussian processes with kernel in the form:
منابع مشابه
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
In many sequential prediction and decision-making problems such as Bayesian filtering and probabilistic model-based planning and control, we need to cope with the challenge of prediction under uncertainty, where the goal is to compute the predictive distribution p(y) given a input distribution p(x) and a probabilistic model p(y|x). Computing the exact predictive distribution is generally intrac...
متن کاملA NOVEL FUZZY-BASED SIMILARITY MEASURE FOR COLLABORATIVE FILTERING TO ALLEVIATE THE SPARSITY PROBLEM
Memory-based collaborative filtering is the most popular approach to build recommender systems. Despite its success in many applications, it still suffers from several major limitations, including data sparsity. Sparse data affect the quality of the user similarity measurement and consequently the quality of the recommender system. In this paper, we propose a novel user similarity measure based...
متن کاملMultiple-step Time Series Forecasting with Sparse Gaussian Processes
Forecasting of non-linear time series is a relevant problem in control. Furthermore, an estimate of the uncertainty of the prediction is useful for constructing robust controllers. Multiple-step ahead forecasting has recently been addressed using Gaussian processes, but direct implementations are restricted to small data sets. In this paper we consider multiple-step forecasting for sparse Gauss...
متن کاملISAR Image Improvement Using STFT Kernel Width Optimization Based On Minimum Entropy Criterion
Nowadays, Radar systems have many applications and radar imaging is one of the most important of these applications. Inverse Synthetic Aperture Radar (ISAR) is used to form an image from moving targets. Conventional methods use Fourier transform to retrieve Doppler information. However, because of maneuvering of the target, the Doppler spectrum becomes time-varying and the image is blurred. Joi...
متن کاملA Total Ratio of Vegetation Index (TRVI) for Shrubs Sparse Cover Delineating in Open Woodland
Persian juniper and Pistachio are grown in low density in the rangelands of North-East of Iran. These rangelands are populated by evergreen conifers, which are widespread and present at low-density and sparse shrub of pistachio in Iran, that are not only environmentally but also genetically essential as seed sources for pistachio improvement in orchards. Rangelands offer excellent opportunities...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017