The Journal of Machine Learning Research
Mining Complex Data
Improving topic evaluation using conceptual knowledge
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Structuring Typical Evolutions Using Temporal-Driven Constrained Clustering
ICTAI '12 Proceedings of the 2012 IEEE 24th International Conference on Tools with Artificial Intelligence - Volume 01
Unsupervised feature construction for improving data representation and semantics
Journal of Intelligent Information Systems
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The objective of the thesis is to explore how complex data can be treated using unsupervised machine learning techniques, in which additional information is injected to guide the exploratory process. Starting from specific problems, our contributions take into account the different dimensions of the complex data: their nature (image, text), the additional information attached to the data (labels, structure, concept ontologies) and the temporal dimension. A special attention is given to data representation and how additional information can be leveraged to improve this representation.