---
title: "Robust Iterative Quantization for Efficient ℓp-norm Similarity Search"
authors:
  - "Yuchen Guo"
  - "Guiguang Ding"
  - "Jungong Han"
  - "Xiaoming Jin"
doi: ""
url: "https://ijcai.org/proceedings/2016/478"
pdf: "https://ijcai.org/proceedings/2016/0478.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3382-3387"
track: ""
keywords:
---

# Robust Iterative Quantization for Efficient ℓp-norm Similarity Search

Yuchen Guo, Guiguang Ding, Jungong Han, Xiaoming Jin

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3382-3387.

## Abstract

Iterative Quantization (ITQ) is one of the most successful hashing based nearest-neighbor search methods for large-scale information retrieval in the past a few years due to its simplicity and superior performance. However, the performance of this algorithm degrades significantly when dealing with noisy data. Additionally, it can barely facilitate a wide range of applications as the distortion measurement only limits to ℓ2 norm. In this paper, we propose an ITQ+ algorithm, aiming to enhance both robustness and generalization of the original ITQ algorithm. Specifically, a ℓp,q-norm loss function is proposed to conduct the ℓp-norm similarity search, rather than a ℓ2} norm search. Despite the fact that changing the loss function to ℓp,q-norm makes our algorithm more robust and generic, it brings us a challenge that minimizes the obtained orthogonality constrained ℓp,q-norm function, which is non-smooth and non-convex. To solve this problem, we propose a novel and efficient optimization scheme. Extensive experiments on benchmark datasets demonstrate that ITQ+ is overwhelmingly better than the original ITQ algorithm, especially when searching similarity in noisy data.