---
title: "Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation"
authors:
  - "Mihajlo Grbovic"
  - "Nemanja Djuric"
  - "Slobodan Vucetic"
doi: ""
url: "https://ijcai.org/proceedings/2013/204"
pdf: "https://ijcai.org/proceedings/2013/0204.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "1358-1363"
track: ""
keywords:
---

# Multi-Prototype Label Ranking with Novel Pairwise-to-Total-Rank Aggregation

Mihajlo Grbovic, Nemanja Djuric, Slobodan Vucetic

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 1358-1363.

## Abstract

We propose a multi-prototype-based algorithm for online learning of soft pairwise-preferences over labels. The algorithm learns soft label preferences via minimization of the proposed soft rank-loss measure, and can learn from total orders as well as from various types of partial orders. The soft pairwise preference algorithm outputs are further aggregated to produce a total label ranking prediction using a novel aggregation algorithm that outperforms existing aggregation solutions. Experiments on synthetic and real-world data demonstrate state-of-the-art performance of the proposed model.