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https://www.um.edu.mt/library/oar/handle/123456789/125527
Title: | Common mode patterns for supervised tensor subspace learning |
Authors: | Makantasis, Konstantinos Doulamis, Anastasios Doulamis, Nikolaos Voulodimos, Athanasios |
Keywords: | Calculus of tensors Dimension reduction (Statistics) Pattern recognition systems Tensor algebra Hyperspectral imaging -- Data processing |
Issue Date: | 2019-05 |
Publisher: | Institute of Electrical and Electronics Engineers |
Citation: | Makantasis, K., Doulamis, A., Doulamis, N., & Voulodimos, A. (2019, May). Common mode patterns for supervised tensor subspace learning. ICASSP 2019, IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton. 2927-2931. |
Abstract: | In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework. The proposed Common Mode Patterns method takes into consideration the labels’ information, and ensures that tensor objects that belong to different classes do not share common features after the reduction of their dimensionality. We experimentally validate the proposed supervised subspace learning technique and compared it against Multilinear Principal Component Analysis using a publicly available hyperspectral imaging dataset. Experimental results indicate that the proposed CMP method can efficiently reduce the dimensionality of tensor objects, while, at the same time, increasing the inter-class separability. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/125527 |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
File | Description | Size | Format | |
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Common mode patterns for supervised tensor subspace learning 2019.pdf Restricted Access | 1.25 MB | Adobe PDF | View/Open Request a copy |
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