Unsupervised Multi-Index Semantic Hashing
- URL: http://arxiv.org/abs/2103.14460v1
- Date: Fri, 26 Mar 2021 13:33:48 GMT
- Title: Unsupervised Multi-Index Semantic Hashing
- Authors: Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Stephen Alstrup,
Christina Lioma
- Abstract summary: We propose an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing.
We experimentally compare MISH to state-of-the-art semantic hashing baselines in the task of document similarity search.
We find that even though multi-index hashing also improves the efficiency of the baselines compared to a linear scan, they are still upwards of 33% slower than MISH.
- Score: 23.169142004594434
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Semantic hashing represents documents as compact binary vectors (hash codes)
and allows both efficient and effective similarity search in large-scale
information retrieval. The state of the art has primarily focused on learning
hash codes that improve similarity search effectiveness, while assuming a
brute-force linear scan strategy for searching over all the hash codes, even
though much faster alternatives exist. One such alternative is multi-index
hashing, an approach that constructs a smaller candidate set to search over,
which depending on the distribution of the hash codes can lead to sub-linear
search time. In this work, we propose Multi-Index Semantic Hashing (MISH), an
unsupervised hashing model that learns hash codes that are both effective and
highly efficient by being optimized for multi-index hashing. We derive novel
training objectives, which enable to learn hash codes that reduce the candidate
sets produced by multi-index hashing, while being end-to-end trainable. In
fact, our proposed training objectives are model agnostic, i.e., not tied to
how the hash codes are generated specifically in MISH, and are straight-forward
to include in existing and future semantic hashing models. We experimentally
compare MISH to state-of-the-art semantic hashing baselines in the task of
document similarity search. We find that even though multi-index hashing also
improves the efficiency of the baselines compared to a linear scan, they are
still upwards of 33% slower than MISH, while MISH is still able to obtain
state-of-the-art effectiveness.
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