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Proceedings Article
Fast Dictionary Learning with a Smoothed Wasserstein Loss
TL;DR: This work proposes to use the Wasserstein distance as the fitting error between each original point and its reconstruction, and proposes scalable algorithms to do so, which improves not only speed but also computational stability.
Proceedings ArticleDOI
Deep NMF for speech separation
TL;DR: Deep NMF is proposed, a novel non-negative deep network architecture which results from unfolding the NMF iterations and untying its parameters, which improves in terms of accuracy upon NMF and is competitive with conventional sigmoid deep neural networks, while requiring a tenth of the number of parameters.
Journal ArticleDOI
Transfer learning in heterogeneous collaborative filtering domains
Weike Pan,Qiang Yang +1 more
TL;DR: This paper presents a novel framework of Transfer by Collective Factorization (TCF), in which a shared latent space is constructed collectively and the data-dependent effect is captured separately to increase the overall quality of knowledge transfer.
Journal ArticleDOI
Muscle synergies as a predictive framework for the EMG patterns of new hand postures.
A B Ajiboye,Richard F. ff. Weir +1 more
TL;DR: It is suggested that global muscle coordination may be a combination of higher order control of robust subject-specific muscle synergies and lower order controlof individuated muscles, and that this control paradigm may be useful in the control of EMG-based technologies, such as artificial limbs and functional electrical stimulation systems.
Journal ArticleDOI
Minimum Dispersion Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data
TL;DR: The derived algorithm, called MiniDisCo, is shown to be particularly robust to the presence of flat spectra, to a possible a priori overestimation of the number of endmembers, or if the amount of observed spectral pixels is low.
Related Papers (5)
Learning the parts of objects by non-negative matrix factorization
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values†
Pentti Paatero,Unto Tapper +1 more