Details

Autor(en) / Beteiligte

• Helm, Hayden S.
• Basu, Amitabh
• Athreya, Avanti
• Park, Youngser
• Vogelstein, Joshua T.
• Priebe, Carey E.
• Winding, Michael
• Zlatic, Marta
• Cardona, Albert
• Bourke, Patrick
• Larson, Jonathan
• Abdin, Marah
• Choudhury, Piali
• Yang, Weiwei
• White, Christopher W.

Titel

Distance-based positive and unlabeled learning for ranking

Ist Teil von

• Pattern recognition, 2023-02, Vol.134, p.109085, Article 109085

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •We propose a model-less ranking method when multiple representations of the data are available.•We study the method in principled simulation settings that could be amenable for future analysis in follow-up work.•We evaluate the method in realistic settings and demonstrate that it is preferred to the existing techniques that handle distance-based ranking. Learning to rank – producing a ranked list of items specific to a query and with respect to a set of supervisory items – is a problem of general interest. The setting we consider is one in which no analytic description of what constitutes a good ranking is available. Instead, we have a collection of representations and supervisory information consisting of a (target item, interesting items set) pair. We demonstrate analytically, in simulation, and in real data examples that learning to rank via combining representations using an integer linear program is effective when the supervision is as light as “these few items are similar to your item of interest.” While this nomination task is quite general, for specificity we present our methodology from the perspective of vertex nomination in graphs. The methodology described herein is model agnostic.