  Unsupervised learning permits the development of algorithms that are able to
adapt to a variety of different data sets using the same underlying rules
thanks to the autonomous discovery of discriminating features during training.
Recently, a new class of Hebbian-like and local unsupervised learning rules for
neural networks have been developed that minimise a similarity matching
cost-function. These have been shown to perform sparse representation learning.
This study tests the effectiveness of one such learning rule for learning
features from images. The rule implemented is derived from a nonnegative
classical multidimensional scaling cost-function, and is applied to both single
and multi-layer architectures. The features learned by the algorithm are then
used as input to an SVM to test their effectiveness in classification on the
established CIFAR-10 image dataset. The algorithm performs well in comparison
to other unsupervised learning algorithms and multi-layer networks, thus
suggesting its validity in the design of a new class of compact, online
learning networks.
