Using Algebra of Hyper-Dimensional Vectors for Heuristic Representation of Data While Training Wide Neural Networks

The method of heuristic data representation in broad artificial neural networks training using the algebra of binary vectors of large length (hyper-size vectors) is offered in the article. The idea of data decentralization in a binary vector is the basis for a data hyper-size representation. The usage of a traditional data structure presupposes that a binary vector can be divided into two parts where each of them stores some specific value of a data structure field. Such vector division is impossible in a decentralized representation. Each bit of a hyper-size vector stores to some extent structure fields all at once and at the same time all bits of a hyper-size vector are necessary for getting a specific value of a field. Such a representation of data is similar to a traditional one for brain natural neural networks. According to the authors' view point its application will allow to improve the recognition quality and in the future it can lead to the creation of crucially new methodology of machine training and artificial intelligence.