Multiview Representation Learning from Crowdsourced Triplet Comparisons
This addresses the challenge of handling multiple independent attributes in representation learning from human comparisons, which is incremental as it builds on prior work by enabling inductive prediction and handling view preferences.
The paper tackles the problem of learning multiview embeddings from crowdsourced triplet comparisons, where different views correspond to independent attributes like color and shape, and proposes an end-to-end inductive deep learning framework that can predict embeddings for new samples, showing improved performance over baseline methods on two crowdsourced datasets.
Crowdsourcing has been used to collect data at scale in numerous fields. Triplet similarity comparison is a type of crowdsourcing task, in which crowd workers are asked the question ``among three given objects, which two are more similar?'', which is relatively easy for humans to answer. However, the comparison can be sometimes based on multiple views, i.e., different independent attributes such as color and shape. Each view may lead to different results for the same three objects. Although an algorithm was proposed in prior work to produce multiview embeddings, it involves at least two problems: (1) the existing algorithm cannot independently predict multiview embeddings for a new sample, and (2) different people may prefer different views. In this study, we propose an end-to-end inductive deep learning framework to solve the multiview representation learning problem. The results show that our proposed method can obtain multiview embeddings of any object, in which each view corresponds to an independent attribute of the object. We collected two datasets from a crowdsourcing platform to experimentally investigate the performance of our proposed approach compared to conventional baseline methods.