Near-Optimal Comparison Based Clustering

Michaël Perrot (1) , Pascal Mattia Esser (2) , Debarghya Ghoshdastidar (2)

1 LHC - Laboratoire Hubert Curien
2 LRR-TUM - Institut für Informatik

The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. However, far less is known when this information is not readily available and, instead, one only observes ordinal comparisons such as "object i is more similar to j than to k." In this paper, we tackle this problem using a two-step procedure: we estimate a pairwise similarity matrix from the comparisons before using a clustering method based on semi-definite programming (SDP). We theoretically show that our approach can exactly recover a planted clustering using a near-optimal number of passive comparisons. We empirically validate our theoretical findings and demonstrate the good behaviour of our method on real data.

Domaines: Apprentissage [cs.LG]

Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar. Near-Optimal Comparison Based Clustering. Neural Information Processing Systems, Dec 2020, Vancouver (virtual), Canada. ⟨hal-03106482⟩