Title :
Joint Entropy Maximization in the Kernel-Based Linear Manifold Topographic Map
Author :
Adibi, Peyman ; Safabakhsh, Reza
Author_Institution :
Amirkabir Univ. of Technol., Tehran
Abstract :
This paper introduces the kernel-based linear manifold topographic map and an information theoretic algorithm developed for its learning. The kernels represent lower dimensional local linear manifolds in a data space, and are defined in an optimal manner when special Gaussian input densities are assumed. The kernel parameters are adapted to maximize the joint entropy of the neuron outputs of the map. This is fulfilled by applying stochastic gradient ascent to the differential entropy of each neuron output and using competition between the neurons of the map. Topology preserving property is also possible by considering neighborhood functions. The proposed model can be considered as an improved version of the ASSOM network which maintains the ASSOM advantages while avoiding its limitations.
Keywords :
Gaussian processes; entropy; gradient methods; learning (artificial intelligence); neural nets; topology; ASSOM network; Gaussian input densities; differential entropy; information theoretic algorithm; joint entropy maximization; kernel-based linear manifold topographic map; learning; stochastic gradient ascent; topology; Entropy; Light emitting diodes; Positron emission tomography; Superluminescent diodes;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371117