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Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances
Brain is the controlling center of our body. With the advent of time, newer and newer brain
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
diseases are being discovered. Thus, because of the variability of brain diseases, existing …
Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that
can map the spatial distribution of molecules with high chemical specificity. IMS does not …
can map the spatial distribution of molecules with high chemical specificity. IMS does not …
Nonnegative matrix factorization with the Itakura-Saito divergence: With application to music analysis
This letter presents theoretical, algorithmic, and experimental results about nonnegative
matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We describe how IS-NMF is …
matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We describe how IS-NMF is …
Smooth PARAFAC decomposition for tensor completion
In recent years, low-rank based tensor completion, which is a higher order extension of
matrix completion, has received considerable attention. However, the low-rank assumption …
matrix completion, has received considerable attention. However, the low-rank assumption …
Static and dynamic source separation using nonnegative factorizations: A unified view
Source separation models that make use of nonnegativity in their parameters have been
gaining increasing popularity in the last few years, spawning a significant number of …
gaining increasing popularity in the last few years, spawning a significant number of …
Enforcing harmonicity and smoothness in Bayesian non-negative matrix factorization applied to polyphonic music transcription
This paper presents theoretical and experimental results about constrained non-negative
matrix factorization (NMF) in a Bayesian framework. A model of superimposed Gaussian …
matrix factorization (NMF) in a Bayesian framework. A model of superimposed Gaussian …
Extended SMART algorithms for non-negative matrix factorization
In this paper we derive a family of new extended SMART (Simultaneous Multiplicative
Algebraic Reconstruction Technique) algorithms for Non-negative Matrix Factorization …
Algebraic Reconstruction Technique) algorithms for Non-negative Matrix Factorization …
Analysis of financial data using non-negative matrix factorization
Abstract We apply Non-negative Matrix Factorization (NMF) to the prob-lem of identifying
underlying trends in stock market data. NMF is arecent and very successful tool for data …
underlying trends in stock market data. NMF is arecent and very successful tool for data …
Probabilistic Stone's Blind Source Separation with application to channel estimation and multi-node identification in MIMO IoT green communication and multimedia …
By the increasing growth of the Internet of Things (IoT) which provides interconnection and
communications between electronic devices and corresponding sensors, a large volume of …
communications between electronic devices and corresponding sensors, a large volume of …
Majorization-minimization algorithm for smooth Itakura-Saito nonnegative matrix factorization
C Févotte - 2011 IEEE International Conference on Acoustics …, 2011 - ieeexplore.ieee.org
Nonnegative matrix factorization (NMF) with the Itakura-Saito divergence has proven
efficient for audio source separation and music transcription, where the signal power …
efficient for audio source separation and music transcription, where the signal power …