Treffer: TensorClus: A python library for tensor (Co)-clustering

Title:
TensorClus: A python library for tensor (Co)-clustering
Contributors:
University of Stuttgart = Universität Stuttgart, CB - Centre Borelli - UMR 9010 (CB), Service de Santé des Armées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-Université Paris Cité (UPCité)
Source:
Neurocomputing. 468:464-468
Publisher Information:
CCSD; Elsevier, 2022.
Publication Year:
2022
Collection:
collection:INSERM
collection:SSA
collection:CNRS
collection:ENS-CACHAN
collection:INSMI
collection:UNIV-PARIS-SACLAY
collection:UNIV-PARIS
collection:UNIVERSITE-PARIS
collection:UP-SCIENCES
collection:UNIVERSITE-PARIS-SACLAY
collection:CGB
collection:ENS-PARIS-SACLAY
collection:ENS-PSACLAY
collection:CB_UMR9010
collection:GS-MATHEMATIQUES
Original Identifier:
HAL: hal-03672607
Document Type:
Zeitschrift article<br />Journal articles
Language:
English
ISSN:
0925-2312
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neucom.2021.09.036
DOI:
10.1016/j.neucom.2021.09.036
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.03672607v1
Database:
HAL

Weitere Informationen

Tensor data analysis is the evolutionary step of data analysis to more than two dimensions. Dealing with tensor data is often based on tensor decomposition methods. The present paper focuses on unsupervised learning and provides a python package referred to as TensorClus including novel co-clustering algorithms of three-way data. All proposed algorithms are based on the latent block models and suitable to different types of data, sparse or not. They are successfully evaluated on challenges in text mining, recommender systems, and hyperspectral image clustering. TensorClus is an open-source Python package that allows easy interaction with other python packages such as NumPy and TensorFlow; it also offers an interface with some tensor decomposition packages namely Tensorly and TensorD on the one hand, and on the other, the co-clustering package Coclust. Finally, it provides CPU and GPU compatibility. The TensorClus library is available at https://pypi.org/project/TensorClus/ 1 .