2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDMLSP-19.3
Paper Title KERNEL ORTHOGONAL NONNEGATIVE MATRIX FACTORIZATION: APPLICATION TO MULTISPECTRAL DOCUMENT IMAGE DECOMPOSITION
Authors Abderrahmane Rahiche, Mohamed Cheriet, Synchromedia Lab, Ecole de Technologie Supérieure (ETS), Canada
SessionMLSP-19: Non-Negative Matrix Factorization
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-MFC] Matrix factorizations/completion
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract As a nonlinear extension of the standard nonnegative matrix factorization (NMF), kernel-based variants have demonstrated to be more effective for discovering meaningful latent features from raw data. However, many existing kernel methods allow only obtaining the basis matrix in the projected feature space, which prevents its inverse mapping back to the original space as requested in many applications. In this work, we propose a new kernel orthogonal NMF method that does not suffer from the pre-image issue. We incorporate the orthogonality constraint as an optimization problem over the Stiefel manifold to improve the sparsity and the model's clustering properties. We solve the proposed model with an efficient optimization approach based on the alternating direction method of multipliers (ADMM) scheme and the projected gradients method. We validate our model on the task of blind decomposition of real-world Multispectral (MS) document images. Our experiments demonstrate the competitiveness of our proposed model in comparison to the state-of-the-art techniques.