  Proposed in 1991, Least Mean Square Error Reconstruction for self-organizing
network, shortly Lmser, was a further development of the traditional
auto-encoder (AE) by folding the architecture with respect to the central
coding layer and thus leading to the features of symmetric weights and neurons,
as well as jointly supervised and unsupervised learning. However, its
advantages were only demonstrated in a one-hidden-layer implementation due to
the lack of computing resources and big data at that time. In this paper, we
revisit Lmser from the perspective of deep learning, develop Lmser network
based on multiple convolutional layers, which is more suitable for
image-related tasks, and confirm several Lmser functions with preliminary
demonstrations on image recognition, reconstruction, association recall, and so
on. Experiments demonstrate that Lmser indeed works as indicated in the
original paper, and it has promising performance in various applications.
