A method and
system for creating hypercomplex representations of data includes, in one exemplary embodiment, at least one set of training data with associated labels or desired response values, transforming the data and labels into hypercomplex values, methods for defining hypercomplex graphs of functions, training algorithms to minimize the cost of an
error function over the parameters in the graph, and methods for reading hierarchical data representations from the resulting graph. Another exemplary embodiment learns hierarchical representations from unlabeled data. The method and
system, in another exemplary embodiment, may be employed for biometric identity
verification by combining
multimodal data collected using many sensors, including, data, for example, such as anatomical characteristics, behavioral characteristics, demographic indicators, artificial characteristics. In other exemplary embodiments, the
system and method may learn hypercomplex function approximations in one environment and transfer the learning to other target environments. Other exemplary applications of the hypercomplex
deep learning framework include:
image segmentation;
image quality evaluation; image
steganalysis; face recognition; event embedding in
natural language processing;
machine translation between languages; object recognition; medical applications such as
breast cancer mass classification; multispectral imaging; audio
processing;
color image filtering; and clothing identification.