@inproceedings{nouboukpo_weakly_2021,

title = {Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints},

author = {A. Nouboukpo and M. S. Allili},

editor = {Premaratne K. Benferhot S. Antonucci A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131143535&doi=10.32473%2fflairs.v34i1.128490&partnerID=40&md5=21cda84d36649f4835be079ea2566717},

doi = {10.32473/flairs.v34i1.128490},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS},

volume = {34},

publisher = {Florida Online Journals, University of Florida},

abstract = {We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes the number of classes is known but does not assume any prior knowledge about the number of mixture components in each class. Therefore, our model: (1) allocates multiple mixture components to individual classes, (2) estimates automatically the number of components of each class, 3) propagates class labels to unlabelled data in a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasets show the robustness and performance of our model over state-of-the-art methods. © 2021 by the authors. All rights reserved.},

note = {ISSN: 23340754},

keywords = {Classification and clustering, Group structure, Learn+, Mixture components, Mixture modeling, Mixtures, Multilevels, Number of class, Prior-knowledge, Semi-supervised learning, Supervised learning, Unlabeled data},

pubstate = {published},

tppubtype = {inproceedings}

}