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Journal ArticleDOI
Sparse and Non-Negative BSS for Noisy Data
TL;DR: The proposed algorithm, named nGMCA (non-negative Generalized Morphological Component Analysis), makes use of proximal calculus techniques to provide properly constrained solutions and is shown to provide robustness to noise and performs well on synthetic mixtures of real NMR spectra.
Proceedings Article
Application of Non-negative Matrix Factorization to fluorescence spectroscopy
TL;DR: Application of signal processing and chemometric techniques to fluorescence spectroscopy to reconstruct the pure components spectra because of their mutually statistically dependence is analyzed.
Journal ArticleDOI
Experimental Study on Extreme Learning Machine Applications for Speech Enhancement
TL;DR: The experimental results indicate that when the amount of training data is limited, both ELM- and H-ELM-based speech enhancement techniques consistently outperform the conventional BP-based shallow and deep learning algorithms, in terms of standardized objective evaluations, under various testing conditions.
Proceedings Article
Recommending groups to users using user-group engagement and time-dependent matrix factorization
TL;DR: The experiments indicate that the time-varying implicit engagement-based model provides the best top-K group recommendations, illustrating the benefit of the added model complexity.
Proceedings ArticleDOI
Enhancing Item Response Theory for Cognitive Diagnosis.
Song Cheng,Qi Liu +1 more
TL;DR: A general Deep Item Response Theory (DIRT) framework to enhance traditional IRT for cognitive diagnosis by exploiting semantic representation from question texts with deep learning and design a deep diagnosis module to diagnose parameters in traditional I RT by deep learning techniques.
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