A conservative aggressive subspace tracker
Koby Crammer · 2007
The need to track a subspace accurately describing well a stream of points arises in many signal processing applications. In this work, we present a very efficient algorithm using a machine learning approach, which its goal is to de-noise the stream of input points. The algorithm guarantees the orthonormality of the representation it uses. We demonstrate the merits of our approach using simulations.