Machine Learning on Spherical Manifold
There are a variety of application where an \(n\)-dimensional hypersphere is a natural domain for data. For example images from 360-degrees cameras or weather on the Earth. But basic machine learning algorithms are expected to take place on regular Euclidean space, therefore it is hard to preserve the spherical structure of the domen. In order to overcome this difficulty, we can introduce additional structure to basic optimization algorithms like Gradient Descent, to enforce them to work on sphere.