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》 资源 》论文》Semi-Supervised Structuring of Complex Data∗ Marian-Andrei Rizoiu

Semi-Supervised Structuring of Complex Data∗ Marian-Andrei Rizoiu

2019-11-09 | |  101 |   53 |   0

Abstract
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.

上一篇：Semi-Supervised Learning for Integration of Aerosol Predictions from Multiple Satellite Instruments

下一篇：Robust Unsupervised Feature Selection Mingjie Qian and Chengxiang Zhai

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