Boosting for Comparison-Based Learning

@inproceedings{Perrot2019BoostingFC,
  title={Boosting for Comparison-Based Learning},
  author={Micha{\"e}l Perrot and Ulrike von Luxburg},
  booktitle={IJCAI},
  year={2019}
}
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 A is closer to object B than to object C.'' 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… 
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Citation Type

Classification from Triplet Comparison Data
TLDR
This letter proposes an unbiased estimator for the classification risk under the empirical risk minimization framework, which inherently has the advantage that any surrogate loss function and any model, including neural networks, can be easily applied.
Efficient Data Analytics on Augmented Similarity Triplets
TLDR
This work gives an efficient method of augmenting the triplets data, by utilizing additional implicit information inferred from the existing data, and proposes a novel set of algorithms for common supervised and unsupervised machine learning tasks based on triplets.
Comparison-based centrality measures
TLDR
This paper systematically investigate comparison-based centrality measures on triplets and theoretically analyze their underlying Euclidean notion of centrality, and proposes a third measure, which is a natural compromise between these two.
Partitioned K-nearest neighbor local depth for scalable comparison-based learning
TLDR
Partitioned Nearest Neighbors Local Depth is introduced, a computationally tractable variant of PaLD leveraging the K-nearest neighbors digraph on S and shows that the probability of randomization-induced error δ in PaNNLD is no more than 2e−δ K.
Learning from Aggregate Observations
TLDR
This paper presents a probabilistic framework that is applicable to a variety of aggregate observations, e.g., pairwise similarity for classification and mean/difference/rank observation for regression.

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