2 research outputs found
Tensor-based Nonlinear Classifier for High-Order Data Analysis
In this paper we propose a tensor-based nonlinear model for high-order data
classification. The advantages of the proposed scheme are that (i) it
significantly reduces the number of weight parameters, and hence of required
training samples, and (ii) it retains the spatial structure of the input
samples. The proposed model, called \textit{Rank}-1 FNN, is based on a
modification of a feedforward neural network (FNN), such that its weights
satisfy the {\it rank}-1 canonical decomposition. We also introduce a new
learning algorithm to train the model, and we evaluate the \textit{Rank}-1 FNN
on third-order hyperspectral data. Experimental results and comparisons
indicate that the proposed model outperforms state of the art classification
methods, including deep learning based ones, especially in cases with small
numbers of available training samples.Comment: To appear in IEEE ICASSP 2018. arXiv admin note: text overlap with
arXiv:1709.0816