NON-EUCLIDEAN VECTOR PRODUCT FOR NEURAL NETWORKS

Date

2018-04-20

Author

Afrasiyabi, Arman
Badawi, Diaa
Nasır, Barış
Yildiz, Ozan
Yarman Vural, Fatoş Tunay
ÇETİN, AHMET ENİS

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We present a non-Euclidean vector product for artificial neural networks. The vector product operator does not require any multiplications while providing correlation information between two vectors. Ordinary neurons require inner product of two vectors. We propose a class of neural networks with the universal approximation property over the space of Lebesgue integrable functions based on the proposed non-Euclidean vector product. In this new network, the "product" of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This "product' is used to construct a vector product in R-N. The vector product induces the l(1) norm. The additive neural network successfully solves the XOR problem. Experiments on MNIST and CIFAR datasets show that the classification performance of the proposed additive neural network is comparable to the corresponding multi-layer perceptron and convolutional neural networks.

Subject Keywords

Non-Euclidean operator, Additive neural networks, Multiplication-free operator

URI

https://hdl.handle.net/11511/55281

Conference Name

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Afrasiyabi, D. Badawi, B. Nasır, O. Yildiz, F. T. Yarman Vural, and A. E. ÇETİN, “NON-EUCLIDEAN VECTOR PRODUCT FOR NEURAL NETWORKS,” presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, CANADA, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55281.