Learning to Hash on Structured Data
Authors: Qifan Wang, Luo Si, Bin Shen
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two datasets clearly demonstrate the advantages of the proposed method over several state-of-the-art hashing methods. |
| Researcher Affiliation | Academia | Qifan Wang, Luo Si and Bin Shen Computer Science Department, Purdue University West Lafayette, IN 47907, US wang868@purdue.edu, lsi@purdue.edu, bshen@purdue.edu |
| Pseudocode | Yes | Algorithm 1 Hashing on Structured Data (HSD) |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of its source code. |
| Open Datasets | Yes | Web KB2 contains 8280 webpages in total collected from four universities. The webpages without any incoming and outgoing links are deleted, resulting in a subset of 6883 webpages. The tf-idf (normalized term frequency and log inverse document frequency) (Manning, Raghavan, and Sch utze 2008) features are extracted for each webpage. NUS-WIDE3 (Chua et al. 2009) is created by NUS lab for evaluating image annotation and retrieval techniques. These datasets are referenced with footnotes containing URLs: '2http://www.cs.cmu.edu/ Web KB' and '3http://lms.comp.nus.edu.sg/research/NUS-WIDE.htm'. |
| Dataset Splits | Yes | The parameter α and β are tuned by 5-fold cross validation through the grid {0.01, 0.1, 1, 10, 100} on the training set and we will discuss more details on how it affects the performance of our approach later. |
| Hardware Specification | Yes | We implement our algorithm using Matlab on a PC with Intel Duo Core i5-2400 CPU 3.1GHz and 8GB RAM. |
| Software Dependencies | No | The paper mentions 'Matlab' but does not specify a version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The parameter α and β are tuned by 5-fold cross validation through the grid {0.01, 0.1, 1, 10, 100} on the training set and we will discuss more details on how it affects the performance of our approach later. |