This is the report of a preliminary study, in which a new coding of persistence diagrams and two relevance feedback methods, designed for use with persistent homology, are combined. The coding consists in substituting persistence diagrams with complex polynomials; these are "shortened", in the sense that only the first few coefficients are used. The relevance feedback methods play on the user's feedback for changing the impact of the diferent filtering functions in determining the output.

Angeli, A., Ferri, M., Monti Eleonora, Tomba, I. (2018). Shortened persistent homology for a biomedical retrieval system with relevance feedback. Springer [10.1007/978-3-319-99740-7_20].

Shortened persistent homology for a biomedical retrieval system with relevance feedback

Angeli, Alessia
Membro del Collaboration Group
;
Ferri, Massimo
Membro del Collaboration Group
;
Tomba, Ivan
Membro del Collaboration Group
2018

Abstract

This is the report of a preliminary study, in which a new coding of persistence diagrams and two relevance feedback methods, designed for use with persistent homology, are combined. The coding consists in substituting persistence diagrams with complex polynomials; these are "shortened", in the sense that only the first few coefficients are used. The relevance feedback methods play on the user's feedback for changing the impact of the diferent filtering functions in determining the output.
2018
Machine Learning and Knowledge Extraction, CD-MAKE 2018
282
292
Angeli, A., Ferri, M., Monti Eleonora, Tomba, I. (2018). Shortened persistent homology for a biomedical retrieval system with relevance feedback. Springer [10.1007/978-3-319-99740-7_20].
Angeli, Alessia; Ferri, Massimo; Monti Eleonora; Tomba, Ivan
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/645130
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