Master's Project: Structural Intrinsic Dimensionality

Diffusion for Machine Learning

Supervision: Stephane Marchand-Maillet

Date of proposal: November 2023

High dimensional data makes learning and analysis difficult due to the Curse of Dimensionality. Here, we are interested in estimating the Local Intrinsic Dimensionality of the data to inform subsequent Machine Learning operations. Structural Intrinsic Dimensionality estimation uses random diffusion over neighborhood graphs so as to capture the structure of these neighborhoods.

The project consists in extending the results from

Marchand-Maillet, S., Pedreira, O., & Chavez, E. (2021). Structural Intrinsic Dimensionality. In Similarity Search and Applications - 14th International Conference (SISAP2021), Dortmund, DE - online.

in relation to the corresponding SNF project

If interested please contact me.

teaching/23-structural.txt · Last modified: 2024/01/23 17:54 by marchand

Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion