Robust Ordinal Embedding from Contaminated Relative Comparisons

TL;DR

This paper introduces a unified framework for robust ordinal embedding that jointly detects contaminated comparisons and learns reliable embeddings, overcoming limitations of traditional multi-stage methods and considering global inconsistency.

Contribution

The proposed method unifies contamination detection and embedding learning, addressing sub-optimality and incorporating global inconsistency in ordinal embedding.

Findings

Effective in simulated and real-world data

Reduces sub-optimal solutions compared to traditional methods

Handles contaminated comparisons robustly

Abstract

Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) The proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency; (3) Instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons.