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Shortened Persistent Homology for a Biomedical Retrieval System with Relevance Feedback

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 different filtering functions in determining the output.
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https://hal.inria.fr/hal-02060046
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Submitted on : Thursday, March 7, 2019 - 10:36:45 AM
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Alessia Angeli, Massimo Ferri, Eleonora Monti, Ivan Tomba. Shortened Persistent Homology for a Biomedical Retrieval System with Relevance Feedback. 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.282-292, ⟨10.1007/978-3-319-99740-7_20⟩. ⟨hal-02060046⟩

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