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 samp...

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Bibliographic Details
Main Authors: Makantasis, Konstantinos, Doulamis, Anastasios, Doulamis, Nikolaos, Nikitakis, Antonis, Voulodimos, Athanasios
Format: Conference Proceeding
Language:English
Subjects:
Data models
hyperspectral data
Hyperspectral imaging
Machine learning
Matrix decomposition
Neurons
Rank-1 FNN
Tensile stress
tensor data analysis
Tensor-based classification
Training
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Summary: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 Rank-1 FNN, is based on a modification of a feedforward neural network (FNN), such that its weights satisfy the rank-1 canonical decomposition. We also introduce a new learning algorithm to train the model, and we evaluate the 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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8461418