Boosting for Comparison-Based Learning
We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form "object x_i is closer to object x_j than to object x_k." In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet comparisons. The main idea is to aggregate the triplets information into weak classifiers, which can subsequently be boosted to a strong classifier. Our method has two main advantages: (i) it is applicable to data from any metric space, and (ii) it can deal with large scale problems using only passively obtained and noisy triplets. We derive theoretical generalization guarantees and a lower bound on the number of necessary triplets, and we empirically show that our method is both competitive with state of the art approaches and resistant to noise.
READ FULL TEXT