Results 191 to 200 of about 13,893 (236)

Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications. [PDF]

Sensors (Basel)
Lakhmi R, Fischer M, Darves-Blanc Q, Alrammouz R, Rieu M, Viricelle JP.   +5 more
europepmc   +1 more source

Erratum to: Mixtures of Quantile-Based Factor Analyzers

Journal of Classification
openaire   +1 more source

Exploring environmental nanobiogeochemistry using field-flow fractionation and ICP-MS-based tools: background and fundamentals.

Environ Sci Nano
Cuss CW, Alasonati E, Benedetti MF, Churchill CM, Fernando S, Gasco R, Goodman A, Moens C, Montaño MD, Slaveykova VI, Tharaud M, Worms IAM.   +11 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms For Hyperspectral Images Classification

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
This paper presents two dimensionality reduction methods, mixtures of factor analyzers (MFA) and deep mixtures of factor analyzers (DMFA), for classification of hyperspectral image (HSI). DMFA consists of two layers of MFA and can extract more information from HSI than MFA can.
Bin Zhao, Magnus O Ulfarsson, Jocelyn Chanussot   +2 more
exaly   +2 more sources

Hyperspectral Images Denoising Based on Mixtures of Factor Analyzers

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
This paper presents two hyperspectral image (HSI) denoising methods, mixtures of factor analyzers (MFA) and wavelet-based MFA (WMFA). MFA uses a Gaussian mixture model to segment the original HSI into different parts, where each part follows Gaussian distribution and then utilizes a factor analyzer to get a low-rank factor loading matrix, and finally ...
Bin Zhao, Magnus O Ulfarsson, Jocelyn Chanussot   +2 more
exaly   +2 more sources

(Semi-) Supervised Mixtures of Factor Analyzers and Deep Mixtures of Factor Analyzers Dimensionality Reduction Algorithms For Hyperspectral Images Classification

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
This paper presents four dimensionality reduction methods, supervised mixtures of factor analyzers (SMFA), semi-supervised mixtures of factor analyzers (S2MFA), supervised deep mixtures of factor analyzers (SDMFA) and semi-supervised deep mixtures of factor analyzers (S2DMFA), for hyperspectral image (HSI) classification. The performance of SMFA, S2MFA,
Bin Zhao, Magnus O Ulfarsson, Jocelyn Chanussot   +2 more
exaly   +2 more sources

Mitigating Outliers for Bayesian Mixture of Factor Analyzers

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2020
The Bayesian mixture of factor analyzers (BMFA), which achieves joint clustering and dimensionality reduction, is with an appealing feature of automatic hyper-parameter learning. In addition to its great success in various unsupervised learning tasks, it exemplifies how the Bayesian statistics can be leveraged to achieve automatic hyper-parameter ...
Zhongtao Chen, Lei Cheng 0003
openaire   +1 more source

Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions

Advances in Data Analysis and Classification, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sharon X. Lee, Tsung-I Lin, Geoffrey J. McLachlan   +2 more
openaire   +4 more sources

Nonparametric mixtures of factor analyzers

2009 IEEE 17th Signal Processing and Communications Applications Conference, 2009
The mixtures of factor analyzers (MFA) model allows data to be modeled as a mixture of Gaussians with a reduced parametrization. We present the formulation of a nonparametric form of the MFA model, the Dirichlet process MFA (DPMFA). The proposed model can be used for density estimation or clustering of high dimensiona data.
Dilan Gorur, Carl Edward Rasmussen
openaire   +1 more source