Self organizing neural networks for structured data

Classification is an essential operation for data analysis tasks: either an unsupervised natural grouping of data items is desired in order to identify potential similarities, or a supervised active class separation is required for prediction problems or decision making. Two well-known and biologically plausible algorithms are the supervised Learning Vector Quantization (LVQ) and the unsupervised Self-Organizing Maps (SOM) proposed by Kohonen.

In the talk, we focus on perspectives of transfering paradigms of self organization to the field of compositionality and structured data.

Extensions of LVQ are discussed: structure can be taken into account by implementing an adaptive metric. Here, we present an intuitive method which involves relevance factors. This additional structure can be used for an efficient rule extraction scheme. Turning prototypes into rules leads to easier data interpretation and constitutes a step towards a hybrid system.

Finally, a brief glance will be thrown at ongoing work about an alternative method for unsupervised learning: incorporating the idea of recurrent and recursive nets into SOM, the data structure is stored in the dynamics itself. This is a new approach to sequence processing and to the treatment of graph structures integrating several techniques like TKM, Recursive SOM, and SOM-SD.