• Corpus ID: 119114308

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)

@article{Gautam2019OCKELMKE,
  title={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)},
  author={Chandan Gautam and Aruna Tiwari and M. Tanveer},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.08338}
}
Kernel method-based one-class classifier is mainly used for outlier or novelty detection. In this letter, kernel ridge regression (KRR) based one-class classifier (KOC) has been extended for learning using privileged information (LUPI). LUPI-based KOC method is referred to as KOC+. This privileged information is available as a feature with the dataset but only for training (not for testing). KOC+ utilizes the privileged information differently compared to normal feature information by using a… 
2 Citations

Figures and Tables from this paper

Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification
In this paper, we suggest a novel approach termed as regularized based implicit Lagrangian twin extreme learning machine in primal as a pair of unconstrained convex minimization problem (RILTELM)

References

SHOWING 1-10 OF 19 REFERENCES
Learning using privileged information: SV M+ and weighted SVM
One-Class Classification with Extreme Learning Machine
TLDR
The experimental evaluation shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one- class classification methods.
Privileged information for data clustering
Learning with Privileged Information for Multi-Label Classification
A new one-class SVM based on hidden information
Support vector data description using privileged information
TLDR
A novel support vector data description which introduces the privileged information to the traditional SVDD is proposed, which will optimise the training phase by constructing a set of correcting functions.
Deep Learning Under Privileged Information Using Heteroscedastic Dropout
TLDR
This work proposes to use a heteroscedastic dropout and make the variance of the dropout a function of privileged information and significantly increases the sample efficiency during learning, resulting in higher accuracy with a large margin when the number of training examples is limited.
A new learning paradigm: Learning using privileged information
One-Class SVM with Privileged Information and Its Application to Malware Detection
TLDR
A new approach to the one-class classification of Malware Classification Challenge using a new problem statement and a corresponding algorithm that allow taking into account a privileged information during the training phase.
Learning to Rank Using Privileged Information
TLDR
This work introduces two maximum-margin techniques that are able to make use of this additional source of information about the training data which however will not be available at test time, and shows that the framework is applicable to several scenarios that have been studied in computer vision before.
...
1
2
...