On subspace projection autoassociative memories based on linear support vector regression

Autossociative memories (AMs) are models inspired by the human brain ability to store and recall information. They should be able to retrieve a stored information upon presentation of a partial or corrupted item. An AM that projects the input onto a linear subspace is called subspace projection autoassociative memory (SPAM). The recall phase of a SPAM model is equivalent to a multi-linear regression problem. In particular, the optimal linear autoassociative memory (OLAM) corresponds to the SPAM model obtained by considering traditional least squares regression in the recall phase. In this paper, we present a novel class of SPAM models obtained by considering linear support vector regression (SVR). Precisely, we introduce three SPAM models based on primal, dual, and bi-level formulations of the linear e-support vector regression. A simple example is used throughout the paper to illustrate the noise tolerance of the proposed memory models.