Cover's theorem

Cover's Theorem is a statement in computational learning theory and is one of the primary theoretical motivations for the use of non-linear kernel methods in machine learning applications. The theorem states that given a set of training data that is not linearly separable, one can with high probability transform it into a training set that is linearly separable by projecting it into a higher-dimensional space via some non-linear transformation.

The proof is easy. A deterministic mapping may be used. Indeed, suppose there are $n$ samples. Lift them onto the vertices of the simplex in the $n-1$ dimensional real space. Every partition of the samples into two sets is separable by a linear separator. QED.

A complex pattern-classification problem, cast in a high-dimensional space nonlinearly, is more likely to be linearly separable than in a low-dimensional space, provided that the space is not densely populated.

— Cover, T.M., Geometrical and Statistical properties of systems of linear inequalities with applications in pattern recognition., 1965

References

• Haykin, Simon (2009). Neural Networks and Learning Machines Third Edition. Upper Saddle River, New Jersey: Pearson Education Inc. pp. 232–236. ISBN 978-0-13-147139-9.
• Cover, T.M. (1965). "Geometrical and Statistical properties of systems of linear inequalities with applications in pattern recognition". IEEE Transactions on Electronic Computers. EC-14: 326–334. doi:10.1109/pgec.1965.264137.
• Mehrotra, K., Mohan, C.K., Ranka, S. (1997) Elements of artificial neural networks, 2nd edition. MIT Press. (Section 3.5) ISBN 0-262-13328-8 Google books