Improving Optimal Linear Associative Memory Using Data Partitioning

Linear associative memory (LAM) has two serious problems to be practical. One is a large space of memory. The other is a high computational complexity. In this paper, we propose partitioning of the input data to alleviate these problems. The optimal linear associative memory (OLAM) minimizes the error of recall employing a pseudo-inverse operation. The proposed algorithm was applied to auto-associative recall of facial images. We show that the proposed algorithm can both reduce the memory space and computational complexity over the conventional optimal linear associative memory.