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

arXiv: https://arxiv.org/abs/2606.22210 · HTML: https://mlsanity.com/paper/2606.22210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 101 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Unifying distillation and privileged information](https://mlsanity.com/paper/1511.03643/) (2015)
- [DADA: Depth-aware Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1904.01886/) (2019)
- [One-Class Classification: Taxonomy of Study and Review of Techniques](https://mlsanity.com/paper/1312.0049/) (2013)
- [Learning with Privileged Information for Efficient Image Super-Resolution](https://mlsanity.com/paper/2007.07524/) (2020)
- [Beyond One-hot Encoding: lower dimensional target embedding](https://mlsanity.com/paper/1806.10805/) (2018)
- [Deep Learning under Privileged Information Using Heteroscedastic Dropout](https://mlsanity.com/paper/1805.11614/) (2018)
- [Person Re-identification with Metric Learning using Privileged Information](https://mlsanity.com/paper/1904.05005/) (2019)
- [Learning Using Privileged Information: SVM+ and Weighted SVM](https://mlsanity.com/paper/1306.3161/) (2013)
- [Learning to Transfer Privileged Information](https://mlsanity.com/paper/1410.0389/) (2014)
- [Privileged Information for Data Clustering](https://mlsanity.com/paper/1305.7454/) (2013)
- [Adaptive SVM+: Learning with Privileged Information for Domain Adaptation](https://mlsanity.com/paper/1708.09083/) (2017)
- [Unsupervised Domain Adaptation via Distilled Discriminative Clustering](https://mlsanity.com/paper/2302.11984/) (2023)
- [One-Class SVM with Privileged Information and its Application to Malware Detection](https://mlsanity.com/paper/1609.08039/) (2016)
- [Efficient learning of nonlinear prediction models with time-series privileged information](https://mlsanity.com/paper/2209.07067/) (2022)
- [Incorporating Privileged Information to Unsupervised Anomaly Detection](https://mlsanity.com/paper/1805.02269/) (2018)
- [Fine-grained Species Recognition with Privileged Pooling: Better Sample Efficiency Through Supervised Attention](https://mlsanity.com/paper/2003.09168/) (2020)
- [OCKELM+: 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)](https://mlsanity.com/paper/1904.08338/) (2019)

## 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.