Multimodal data fusion: an overview of methods, challenges, and prospects
In various disciplines, information about the same phenomenon can be acquired from
different types of detectors, at different conditions, in multiple experiments or subjects …
different types of detectors, at different conditions, in multiple experiments or subjects …
Data fusion in metabolomics using coupled matrix and tensor factorizations
With a goal of identifying biomarkers/patterns related to certain conditions or diseases,
metabolomics focuses on the detection of chemical substances in biological samples such …
metabolomics focuses on the detection of chemical substances in biological samples such …
Source separation in chemical analysis: Recent achievements and perspectives
Since its origins in the mid-1980s, the field of blind source separation (BSS) has attracted
considerable attention within the signal processing community. One of the main reasons for …
considerable attention within the signal processing community. One of the main reasons for …
Geometric unmixing of large hyperspectral images: A barycentric coordinate approach
P Honeine, C Richard - IEEE Transactions on Geoscience and …, 2011 - ieeexplore.ieee.org
In hyperspectral imaging, spectral unmixing is one of the most challenging and fundamental
problems. It consists of breaking down the spectrum of a mixed pixel into a set of pure …
problems. It consists of breaking down the spectrum of a mixed pixel into a set of pure …
Determination and visualization of rotational ambiguity in four-component systems
One of the main problems that limit the use of model-free analysis methods for the resolution
of multivariate data is that usually there is rotational ambiguity in the result. While methods …
of multivariate data is that usually there is rotational ambiguity in the result. While methods …
Quaternion non-negative matrix factorization: Definition, uniqueness, and algorithm
This article introduces quaternion non-negative matrix factorization (QNMF), which
generalizes the usual non-negative matrix factorization (NMF) to the case of polarized …
generalizes the usual non-negative matrix factorization (NMF) to the case of polarized …
Separable quaternion matrix factorization for polarization images
A transverse wave is a wave in which the particles are displaced perpendicular to the
direction of the wave's advance. Examples of transverse waves include ripples on the …
direction of the wave's advance. Examples of transverse waves include ripples on the …
[PDF][PDF] Regularized nonnegative matrix factorization: Geometrical interpretation and application to spectral unmixing
R Zdunek - 2014 - intapi.sciendo.com
Nonnegative Matrix Factorization (NMF) is an important tool in data spectral analysis.
However, when a mixing matrix or sources are not sufficiently sparse, NMF of an observation …
However, when a mixing matrix or sources are not sufficiently sparse, NMF of an observation …
Approximation of feature vectors in nonnegative matrix factorization with Gaussian radial basis functions
R Zdunek - … : 19th International Conference, ICONIP 2012, Doha …, 2012 - Springer
Abstract Nonnegative Matrix Factorization (NMF) is a feature extraction technique that has
already found numerous applications in machine learning and data processing. In some …
already found numerous applications in machine learning and data processing. In some …
Joint analysis of flow cytometry data and fluorescence spectra as a non-negative array factorization problem
The paper presents a novel approach to the processing of flow cytometry data sequences. It
consists in decomposing a sequence of multidimensional probability density functions by …
consists in decomposing a sequence of multidimensional probability density functions by …