We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce size training data, resulting lower storage consumption and computational complexity. Furthermore, can be regarded as regularization technique, improves generalizability learned target functions. demonstrate significant impro...

Climatic zones, representing seasonal variations in temperature (T) and precipitation (P), are generally mapped geographically using discrete classifications with distinct boundaries. However, it is well known that global T P vary continuously space time steep gradients occurring infrequently. The objective of this analysis to use complementary forms dimensionality reduction quantify the spatio...

Dimensionality Reduction process is a means to overcome curse of dimensionality in general. When all features are available together, it is a way to extract knowledge from a population in a big feature space. On the contrary, dimensionality reduction is intriguing when update to feature space is streaming and the question arises whether one could reduce the feature space as and when the feature...