Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
This work addresses the need for multi-faceted similarity measures in applications like image analysis, though it is incremental as it builds on existing multi-view and metric learning frameworks.
The paper tackles the problem of learning multiple view-specific embeddings from triplet comparisons by jointly modeling correlations between views, achieving lower triplet generalization error on datasets including bird images and performing competitively on multi-task metric learning.
Similarity between objects is multi-faceted and it can be easier for human annotators to measure it when the focus is on a specific aspect. We consider the problem of mapping objects into view-specific embeddings where the distance between them is consistent with the similarity comparisons of the form "from the t-th view, object A is more similar to B than to C". Our framework jointly learns view-specific embeddings exploiting correlations between views. Experiments on a number of datasets, including one of multi-view crowdsourced comparison on bird images, show the proposed method achieves lower triplet generalization error when compared to both learning embeddings independently for each view and all views pooled into one view. Our method can also be used to learn multiple measures of similarity over input features taking class labels into account and compares favorably to existing approaches for multi-task metric learning on the ISOLET dataset.