Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204393417> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W3204393417 endingPage "468" @default.
- W3204393417 startingPage "464" @default.
- W3204393417 abstract "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/." @default.
- W3204393417 created "2021-10-11" @default.
- W3204393417 creator A5000157816 @default.
- W3204393417 creator A5007054746 @default.
- W3204393417 creator A5077793498 @default.
- W3204393417 date "2022-01-01" @default.
- W3204393417 modified "2023-10-13" @default.
- W3204393417 title "TensorClus: A python library for tensor (Co)-clustering" @default.
- W3204393417 cites W1963826206 @default.
- W3204393417 cites W1997201895 @default.
- W3204393417 cites W2093857868 @default.
- W3204393417 cites W2552406463 @default.
- W3204393417 cites W2597328883 @default.
- W3204393417 cites W2727551782 @default.
- W3204393417 cites W2800909518 @default.
- W3204393417 cites W2888987473 @default.
- W3204393417 cites W2923996791 @default.
- W3204393417 cites W2927676531 @default.
- W3204393417 cites W3008151628 @default.
- W3204393417 cites W3017209309 @default.
- W3204393417 cites W3128209463 @default.
- W3204393417 doi "https://doi.org/10.1016/j.neucom.2021.09.036" @default.
- W3204393417 hasPublicationYear "2022" @default.
- W3204393417 type Work @default.
- W3204393417 sameAs 3204393417 @default.
- W3204393417 citedByCount "2" @default.
- W3204393417 countsByYear W32043934172022 @default.
- W3204393417 countsByYear W32043934172023 @default.
- W3204393417 crossrefType "journal-article" @default.
- W3204393417 hasAuthorship W3204393417A5000157816 @default.
- W3204393417 hasAuthorship W3204393417A5007054746 @default.
- W3204393417 hasAuthorship W3204393417A5077793498 @default.
- W3204393417 hasBestOaLocation W32043934172 @default.
- W3204393417 hasConcept C124101348 @default.
- W3204393417 hasConcept C153180895 @default.
- W3204393417 hasConcept C154945302 @default.
- W3204393417 hasConcept C155281189 @default.
- W3204393417 hasConcept C199360897 @default.
- W3204393417 hasConcept C202444582 @default.
- W3204393417 hasConcept C2777904410 @default.
- W3204393417 hasConcept C2986737658 @default.
- W3204393417 hasConcept C3018397939 @default.
- W3204393417 hasConcept C33923547 @default.
- W3204393417 hasConcept C41008148 @default.
- W3204393417 hasConcept C459310 @default.
- W3204393417 hasConcept C519991488 @default.
- W3204393417 hasConcept C73555534 @default.
- W3204393417 hasConcept C80444323 @default.
- W3204393417 hasConceptScore W3204393417C124101348 @default.
- W3204393417 hasConceptScore W3204393417C153180895 @default.
- W3204393417 hasConceptScore W3204393417C154945302 @default.
- W3204393417 hasConceptScore W3204393417C155281189 @default.
- W3204393417 hasConceptScore W3204393417C199360897 @default.
- W3204393417 hasConceptScore W3204393417C202444582 @default.
- W3204393417 hasConceptScore W3204393417C2777904410 @default.
- W3204393417 hasConceptScore W3204393417C2986737658 @default.
- W3204393417 hasConceptScore W3204393417C3018397939 @default.
- W3204393417 hasConceptScore W3204393417C33923547 @default.
- W3204393417 hasConceptScore W3204393417C41008148 @default.
- W3204393417 hasConceptScore W3204393417C459310 @default.
- W3204393417 hasConceptScore W3204393417C519991488 @default.
- W3204393417 hasConceptScore W3204393417C73555534 @default.
- W3204393417 hasConceptScore W3204393417C80444323 @default.
- W3204393417 hasLocation W32043934171 @default.
- W3204393417 hasLocation W32043934172 @default.
- W3204393417 hasLocation W32043934173 @default.
- W3204393417 hasLocation W32043934174 @default.
- W3204393417 hasLocation W32043934175 @default.
- W3204393417 hasOpenAccess W3204393417 @default.
- W3204393417 hasPrimaryLocation W32043934171 @default.
- W3204393417 hasRelatedWork W1486009489 @default.
- W3204393417 hasRelatedWork W2093953080 @default.
- W3204393417 hasRelatedWork W2557718140 @default.
- W3204393417 hasRelatedWork W2963838862 @default.
- W3204393417 hasRelatedWork W3201519406 @default.
- W3204393417 hasRelatedWork W4226323523 @default.
- W3204393417 hasRelatedWork W4379256054 @default.
- W3204393417 hasRelatedWork W47805180 @default.
- W3204393417 hasRelatedWork W67092138 @default.
- W3204393417 hasRelatedWork W4225687299 @default.
- W3204393417 hasVolume "468" @default.
- W3204393417 isParatext "false" @default.
- W3204393417 isRetracted "false" @default.
- W3204393417 magId "3204393417" @default.
- W3204393417 workType "article" @default.