@conference {41, title = {Accelerated learning of Generalized Sammon Mappings}, booktitle = {Neural Networks (IJCNN), The 2011 International Joint Conference on}, year = {2011}, note = {

Acceptance rate 75\% (468/620).

}, month = {July}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {San Jose, California, USA }, abstract = {

The Sammon Mapping (SM) has established itself as a valuable tool in dimensionality reduction, manifold learning, exploratory data analysis and, particularly, in data visualization. The SM is capable of projecting high-dimensional data into a low-dimensional space, so that they can be visualized and interpreted. This is accomplished by representing inter-sample dissimilarities in the original space by Euclidean inter-sample distances in the projection space. Recently, Kernel Sammon Mapping (KSM) has been shown to subsume the SM and a few other related extensions to SM. Both of the aforementioned models feature a set of linear weights that are estimated via Iterative Majorization (IM). While IM is significantly faster than other standard gradient-based methods, tackling data sets of larger than moderate sizes becomes a challenging learning task, as IM{\textquoteright}s convergence significantly slows down with increasing data set cardinality. In this paper we derive two improved training algorithms based on Successive Over-Relaxation (SOR) and Parallel Tangents (PARTAN) acceleration, that, while still being first-order methods, exhibit faster convergence than IM. Both algorithms are relatively easy to understand, straightforward to implement and, performance-wise, are as robust as IM. We also present comparative results that illustrate their computational advantages on a set of benchmark problems.

}, keywords = {accelerated learning, Acceleration, Convergence, data visualization, dimensionality reduction, Euclidean inter-sample distances, exploratory data analysis, generalized Sammon mappings, inter-sample dissimilarities, iterative majorization, Kernel, kernel Sammon mapping, learning (artificial intelligence), manifold learning, parallel tangents acceleration, projection space, Prototypes, standard gradient-based methods, Stress, successive over-relaxation, Training}, doi = {10.1109/IJCNN.2011.6033609}, author = {Yinjie Huang and Michael Georgiopoulos and Georgios C. Anagnostopoulos} } @article {44, title = {An Adaptive Multiobjective Approach to Evolving ART Architectures}, journal = {Neural Networks, IEEE Transactions on}, volume = {21}, year = {2010}, month = {April}, pages = {529-550}, abstract = {

In this paper, we present the evolution of adaptive resonance theory (ART) neural network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to simultaneously evolve the weights and the topology of three well-known ART architectures; fuzzy ARTMAP (FAM), ellipsoidal ARTMAP (EAM), and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, and MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit [accuracy on unseen data (generalization) and size (number of categories created)]. MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. Furthermore, MO-GART is shown to be competitive with other popular classifiers, such as classification and regression tree (CART) and support vector machines (SVMs).

}, keywords = {adaptive multiobjective approach, adaptive resonance theory, Algorithms, ART architecture, ART neural nets, ART neural network architecture, Artificial Intelligence, ARTMAP, Automated, category proliferation, classification, classification problem, Computer Simulation, ellipsoidal ARTMAP, evolutionary computation, fuzzy ARTMAP, Fuzzy Logic, fuzzy neural nets, Gaussian ARTMAP, genetic algorithms (GAs), genetic operators, Humans, Machine learning, multiobjective evolutionary approach, multiobjective optimization, neural net architecture, Neural Networks (Computer), Normal Distribution, optimisation, pattern recognition}, issn = {1045-9227}, doi = {10.1109/TNN.2009.2037813}, author = {Assem Kaylani and Michael Georgiopoulos and Mansooreh Mollaghasemi and Georgios C. Anagnostopoulos and Christopher Sentelle and Mingyu Zhong} }