# Hashing Learning with Hyper-Class Representation

arXiv: https://arxiv.org/abs/2206.02334 · HTML: https://mlsanity.com/paper/2206.02334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering](https://mlsanity.com/paper/1811.03195/) (2018)
- [Optimal Hashing-based Time-Space Trade-offs for Approximate Near Neighbors](https://mlsanity.com/paper/1608.03580/) (2016)
- [Domain-Invariant Representation Learning from EEG with Private Encoders](https://mlsanity.com/paper/2201.11613/) (2022)
- [KNN Classification with One-step Computation](https://mlsanity.com/paper/2012.06047/) (2020)
- [Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation](https://mlsanity.com/paper/2203.04156/) (2022)
- [Hyper-Class Representation of Data](https://mlsanity.com/paper/2201.13317/) (2022)
- [Deep Hashing with Hash Center Update for Efficient Image Retrieval](https://mlsanity.com/paper/2106.06269/) (2021)
- [Learning to Hash Naturally Sorts](https://mlsanity.com/paper/2201.13322/) (2022)
- [Fast Supervised Hashing with Decision Trees for High-Dimensional Data](https://mlsanity.com/paper/1404.1561/) (2014)
- [Extended ADMM and BCD for Nonseparable Convex Minimization Models with Quadratic Coupling Terms: Convergence Analysis and Insights](https://mlsanity.com/paper/1508.00193/) (2015)
- [More Efficient Privacy Amplification with Less Random Seeds via Dual Universal Hash Function](https://mlsanity.com/paper/1311.5322/) (2013)
- [Conceptual Data Modeling: Entity-Relationship Models as Thinging Machines](https://mlsanity.com/paper/2109.14717/) (2021)

## Abstract (arXiv)

Existing unsupervised hash learning is a kind of attribute-centered calculation. It may not accurately preserve the similarity between data. This leads to low down the performance of hash function learning. In this paper, a hash algorithm is proposed with a hyper-class representation. It is a two-steps approach. The first step finds potential decision features and establish hyper-class. The second step constructs hash learning based on the hyper-class information in the first step, so that the hash codes of the data within the hyper-class are as similar as possible, as well as the hash codes of the data between the hyper-classes are as different as possible. To evaluate the efficiency, a series of experiments are conducted on four public datasets. The experimental results show that the proposed hash algorithm is more efficient than the compared algorithms, in terms of mean average precision (MAP), average precision (AP) and Hamming radius 2 (HAM2)