In this paper we introduce an improved binary correlation matrix memory (CMM) with better storage capacity when storing sparse fixed weight codes generated with the algorithm of Baum et al. We outline associative memory, and describe the binary correlation matrix memory- a specific example of a distributed associative memory. The importance of the representation used in a CMM for input and output codes is discussed, with specific regard to sparse fixed weight codes. We present an algorithm for generating of fixed weight codes, originally given by Baum et al. The properties of this algorithm are briefly discussed, including possible thresholding functions which could be used when storing these codes in a CMM; L-max and L-wta. Finally, results generated from a series of simulations are used to demonstrate that the use of L-wta as a thresholding function provides an increase in storage capacity over L-max.
|Title of host publication||Lecture Notes in Computer Science|
|Place of Publication||ICANN 2009|
|Number of pages||9|
|Publication status||Published - 16 Sept 2009|
|Name||Lecture Notes in Computer Science|
- associative memory, correlation matrix memory, storage capacity, fixed weight codes, pattern recognition