mlsanityData to 14 Oct 2026

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

arXiv 2606.22210 ↗First posted 20 Jun 2026cs.LG cs.AI stat.MLcited by 0

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Abstract (arXiv)

One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trained model is used to classify or predict test data -- the Learning Using Privileged Information paradigm (LUPI). Sequential Minimal Optimization (SMO) methods have been developed for supervised Support Vector Machines (SVM), unsupervised one-class SVM, and SVM with privileged information (SVM+). The missing brick in this research has long been a one-class SVM with privileged information (OC-SVM+). In this paper, we propose an SMO algorithm for OC-SVM+ that significantly outperforms non-sequential algorithms for training the OC-SVM+ model. Its finite-time convergence is established. The experiments show how privileged information affects a descriptive domain in the space of original features. Comparative benchmark tests demonstrate that our algorithm is superior over interior point algorithms.

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References · 18

  1. 2021Convex optimizationcited by 2,515
  2. 2015Unifying distillation and privileged informationcited by 233
  3. 2019DADA: Depth-aware Domain Adaptation in Semantic Segmentationcited by 97
  4. 2013One-Class Classification: Taxonomy of Study and Review of Techniquescited by 68
  5. 2020Learning with Privileged Information for Efficient Image Super-Resolutioncited by 53
  6. 2018Beyond One-hot Encoding: lower dimensional target embeddingcited by 43
  7. 2018Deep Learning under Privileged Information Using Heteroscedastic Dropoutcited by 27
  8. 2019Person Re-identification with Metric Learning using Privileged Informationcited by 20
  9. 2013Learning Using Privileged Information: SVM+ and Weighted SVMcited by 13
  10. 2014Learning to Transfer Privileged Informationcited by 6
  11. 2013Privileged Information for Data Clusteringcited by 5
  12. 2017Adaptive SVM+: Learning with Privileged Information for Domain Adaptationcited by 5
6 more
  1. 2023Unsupervised Domain Adaptation via Distilled Discriminative Clusteringcited by 5
  2. 2016One-Class SVM with Privileged Information and its Application to Malware Detectioncited by 4
  3. 2022Efficient learning of nonlinear prediction models with time-series privileged informationcited by 4
  4. 2018Incorporating Privileged Information to Unsupervised Anomaly Detectioncited by 2
  5. 2020Fine-grained Species Recognition with Privileged Pooling: Better Sample Efficiency Through Supervised Attentioncited by 2
  6. 2019OCKELM+: Kernel Extreme Learning Machine based One-class Classification using Privileged Information (or KOC+: Kernel Ridge Regression or Least Square SVM with zero bias based One-class Classification using Privileged Information)cited by 1

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