A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural Networks
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Fusion and Multicue Data Matching by Diffusion Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bell's law for the birth and death of computer classes
Communications of the ACM - 50th anniversary issue: 1958 - 2008
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Clustering
Analysis of hyperspectral data with diffusion maps and fuzzy ART
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Activity recognition via classification constrained diffusion maps
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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It is very difficult to analyse large amounts of hyperspectral data. Here we present a method based on reducing the dimensionality of the data and clustering the result in moving toward classification of the data. Dimensionality reduction is done with diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original dataset in order to obtain an efficient representation of data geometric descriptions. Clustering is done using k-means and a neural network clustering theory, Fuzzy ART (FA). The process is done on a subset of core data from AngloGold Ashanti, and compared to results obtained by AngloGold Ashanti's proprietary method. Experimental results show that the proposed methods are promising in addressing the complicated hyperspectral data and identifying the minerals in core samples.