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Hashing Learning with Hyper-Class Representation

arXiv 2206.02334 ↗First posted 6 Jun 2022cs.LGcited by 0

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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)

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.

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References · 14

  1. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  2. 2016A Survey on Learning to Hashcited by 119
  3. 2018Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clusteringcited by 53
  4. 2016Optimal Hashing-based Time-Space Trade-offs for Approximate Near Neighborscited by 18
  5. 2022Domain-Invariant Representation Learning from EEG with Private Encoderscited by 12
  6. 2020KNN Classification with One-step Computationcited by 8
  7. 2022Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptationcited by 8
  8. 2022Hyper-Class Representation of Datacited by 7
  9. 2021Deep Hashing with Hash Center Update for Efficient Image Retrievalcited by 4
  10. 2022Learning to Hash Naturally Sortscited by 4
  11. 2014Fast Supervised Hashing with Decision Trees for High-Dimensional Datacited by 2
  12. 2015Extended ADMM and BCD for Nonseparable Convex Minimization Models with Quadratic Coupling Terms: Convergence Analysis and Insightscited by 2
2 more
  1. 2013More Efficient Privacy Amplification with Less Random Seeds via Dual Universal Hash Functioncited by 1
  2. 2021Conceptual Data Modeling: Entity-Relationship Models as Thinging Machinescited by 1

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