Results 11 to 20 of about 1,590 (175)
Application of Parallel Factor Analysis (PARAFAC) to Electrophysiological Data [PDF]
Frontiers in Neuroinformatics, 2015 The identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task.
Sarah Katharina eSchmitz +9 more
doaj +3 more sources
An Alternating Bayesian Approach to PARAFAC Decomposition of Tensors
IEEE Access, 2018 The PARAllel FACtor (PARAFAC) decomposition is known as one of the most commonly used tools in tensor signal/data processing. Unfortunately, its classical algorithms barely take the potential statistical and/or deterministic prior information of the ...
Ming Shi, Dan Li, Jian Qiu Zhang
doaj +2 more sources
Valorizing Landfill Gas Condensate as an External Carbon Source for Denitrification in Sewage-Landfill Leachate Co-Treatment. [PDF]
Water Environ ResThis study showed that landfill gas condensate can replace conventional liquid carbon sources for denitrification in SBRs, achieving similarly high nitrogen removal without leaving refractory organic residues, while also beneficially reusing a landfill byproduct to improve sustainability and reduce reliance on purchased carbon.
Ahmed MA, Brazil B, Zhang L, Zhao R.
europepmc +2 more sources
Factor Uniqueness of the Structural Parafac Model [PDF]
Psychometrika, 2020 Factor analysis is a well-known method for describing the covariance structure among a set of manifest variables through a limited number of unobserved factors. When the observed variables are collected at various occasions on the same statistical units, the data have a three-way structure and standard factor analysis may fail.
Giordani P., Rocci R., Bove G.
openaire +5 more sources
Overview of constrained PARAFAC models [PDF]
EURASIP Journal on Advances in Signal Processing, 2014 In this paper, we present an overview of constrained PARAFAC models where the constraints model linear dependencies among columns of the factor matrices of the tensor decomposition, or alternatively, the pattern of interactions between different modes of the tensor which are captured by the equivalent core tensor.
Gérard Favier +1 more
openaire +3 more sources
Smooth PARAFAC Decomposition for Tensor Completion [PDF]
IEEE Transactions on Signal Processing, 2016 13 pages, 9 ...
Tatsuya Yokota +2 more
openaire +3 more sources
Machines, 2023 Here, a novel hybrid method of intelligent fault identification within complex mechanical systems was proposed using parallel-factor (PARAFAC) theory and adaptive particle swarm optimization (APSO) for a support vector machine (SVM).
Shaoyi Li +4 more
doaj +1 more source
Parallel Factorization to Implement Group Analysis in Brain Networks Estimation
Sensors, 2023 When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average ...
Andrea Ranieri +5 more
doaj +1 more source
Robust PARAFAC for incomplete data [PDF]
Journal of Chemometrics, 2012 Different methods exist to explore multiway data. In this article, we focus on the widely used PARAFAC (parallel factor analysis) model, which expresses multiway data in a more compact way without ignoring the underlying complex structure. An alternating least squares procedure is typically used to fit the PARAFAC model. It is, however, well known that
Hubert, Mia +2 more
openaire +2 more sources
A New Algorithm to Solve Parafac-Model
Behaviormetrika, 1982 PARAFAC is a three mode factor analytic method developed by R.A. Harshman and is useful for data analysis. The fundamental idea says $${x_{ijk}} \approx \sum\limits_s^S {a_{is}}{b_{js}}{c_{ks}}$$ where xijk is given by measurement for i=1, 2…,I, j=1, 2,…, J, k ...
Hayashi, Chikio, Hayashi, Fumi
openaire +1 more source