A Caltech Library Service

Tensor-based crowdsourced clustering via triangle queries

Korlakai Vinayak, Ramya and Zrnic, Tijana and Hassibi, Babak (2017) Tensor-based crowdsourced clustering via triangle queries. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE , Piscataway, NJ, pp. 2322-2326. ISBN 978-1-5090-4117-6.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


We consider the problem of crowdsourced clustering of a set of items based on queries of the similarity of triple of objects. Such an approach, called triangle queries, was proposed in [1], where it was shown that, for a fixed query budget, it outperforms clustering based on edge queries (i.e, comparing pairs of objects). In [1] the clustering algorithm for triangle and edge queries was identical and each triangle query response was treated as 3 separate edge query responses. In this paper we directly exploit the triangle structure of the responses by embedding them into a 3-way tensor. Since there are 5 possible responses to each triangle query, it is a priori not clear how best to embed them into the tensor. We give sufficient conditions on non-trivial embedding such that the resulting tensor has a rank equal to the underlying number of clusters (akin to what happens with the rank of the adjacency matrix). We then use an alternating least squares tensor decomposition algorithm to cluster a noisy and partially observed tensor and show, through extensive numerical simulations, that it significantly outperforms methods that make use only of the adjacency matrix.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Korlakai Vinayak, Ramya0000-0003-0248-9551
Additional Information:© 2017 IEEE.
Subject Keywords:Crowdsourced Clustering, Tensor Decomposition
Record Number:CaltechAUTHORS:20170621-171615546
Persistent URL:
Official Citation:R. Korlakai Vinayak, T. Zrnic and B. Hassibi, "Tensor-based crowdsourced clustering via triangle queries," 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017, pp. 2322-2326. doi: 10.1109/ICASSP.2017.7952571
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:78446
Deposited By: Kristin Buxton
Deposited On:22 Jun 2017 01:17
Last Modified:03 Oct 2019 18:08

Repository Staff Only: item control page