Feature extraction from data structures with unsupervised recursive neural networks

In the case of static data of high dimension it is often useful to reduce the dimensionality before performing pattern recognition and learning tasks. One of the main reasons for this is that models for lower-dimensional data usually have fewer parameters to be determined. The problem of finding fixed-length vector representations for labelled directed ordered acyclic graphs (DOAGs) can be regarded as a feature extraction problem in which the dimensionality of the input space is infinite. We address the fundamental problem of finding fixed-length vector representations for DOAGs in an unsupervised way using a maximum entropy approach. Some preliminary experiments on image retrieval are reported