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IJCNN
2006
IEEE

SOM-Based Sparse Binary Encoding for AURA Classifier

10 years 4 months ago
SOM-Based Sparse Binary Encoding for AURA Classifier
—The AURA k-Nearest Neighbour classifier associates binary input and output vectors, forming a compact binary Correlation Matrix Memory (CMM). For a new input vector, matching vectors are retrieved and classification is performed on the basis of these recalled vectors. Real-world data is not binary and must therefore be encoded to form the required binary input. Efficient operation of the CMM requires that these binary input vectors are sparse. Current encoding of high dimensional data requires large vectors in order to remain sparse, reducing efficiency. This paper explores an alternative approach that produces shorter sparse codes, allowing more efficient storage of information without degrading the recall performance of the system.
Simon O'Keefe
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IJCNN
Authors Simon O'Keefe
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