title: Kernel map compression for speeding the execution of kernel-based methods.
abstract: The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machines. Unfortunately, after learning, the computational complexity of execution through a kernel is of the order of the size of the training set, which is quite large for many applications. This paper proposes a two-step procedure for arriving at a compact and computationally efficient execution procedure. After learning in the kernel space, the proposed extension exploits the universal approximation capabilities of generalized radial basis function neural networks to efficiently approximate and replace the projections onto the empirical kernel map used during execution. Sample applications demonstrate significant compression of the kernel representation with graceful performance loss.
authors.affiliation: Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. omararif@gmail.com
authors.forename: Omar
authors.initials: O
authors.lastname: Arif
authors.forename: Patricio A
authors.initials: PA
authors.lastname: Vela
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D003661:Decision Support Techniques; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*