A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation

We present a method, which allows to train a Generalized Matrix Learning Vector Quantization (GMLVQ) model for classification using data from several, maybe non-calibrated, sources without explicit transfer learning. This is achieved by using a siamese-like GMLVQ-architecture, which comprises different sets of prototypes for the target classification and for the separation learning of the sources. In this architecture, a linear map is trained by means of GMLVQ for source distinction in the mapping space in parallel to the classification task learning. The respective null-space projection provides a common data representation of the different source data for an all-together classification learning.

Automated summary

This method allows for classification using data from multiple sources without explicit transfer learning by utilizing a siamese-like GMLVQ architecture. The architecture includes different sets of prototypes for target classification and source separation learning, and trains a linear map for source distinction in parallel to the classification task learning.