@conference {41,
	title = {Accelerated learning of Generalized Sammon Mappings},
	booktitle = {Neural Networks (IJCNN), The 2011 International Joint Conference on},
	year = {2011},
	note = {<p>Acceptance rate 75\% (468/620).</p>
},
	month = {July},
	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
	organization = {Institute of Electrical and Electronics Engineers (IEEE)},
	address = {San Jose, California, USA },
	abstract = {<p>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.</p>
},
	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}
}
