Incremental Kernel Null Space Discriminant Analysis for Novelty Detection

@article{Liu2017IncrementalKN,
  title={Incremental Kernel Null Space Discriminant Analysis for Novelty Detection},
  author={Juncheng Liu and Zhouhui Lian and Yi Wang and J. Xiao},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={4123-4131}
}
Novelty detection, which aims to determine whether a given data belongs to any category of training data or not, is considered to be an important and challenging problem in areas of Pattern Recognition, Machine Learning, etc. Recently, kernel null space method (KNDA) was reported to have state-of-the-art performance in novelty detection. However, KNDA is hard to scale up because of its high computational cost. With the ever-increasing size of data, accelerating the implementing speed of KNDA is… Expand
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References

SHOWING 1-10 OF 27 REFERENCES
Kernel Null Space Methods for Novelty Detection
TLDR
This work presents how to apply a null space method for novelty detection, which maps all training samples of one class to a single point, which outperforms all other methods for multi-class novelty detection. Expand
Kernel Fisher Discriminants for Outlier Detection
The problem of detecting atypical objects or outliers is one of the classical topics in (robust) statistics. Recently, it has been proposed to address this problem by means of one-class SVMExpand
Null space-based kernel Fisher discriminant analysis for face recognition
  • Wei Liu, Yunhong Wang, S. Li, T. Tan
  • Mathematics, Computer Science
  • Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings.
  • 2004
TLDR
From the theoretical analysis, the NLDA algorithm and the most suitable situation for NLDA are presented and the method is simpler than all other null space approaches, it saves the computational cost and maintains the performance simultaneously. Expand
Fast incremental LDA feature extraction
TLDR
New algorithms to accelerate the convergence rate of the incremental LDA algorithm given by Chatterjee and Roychowdhury are derived by optimizing the step size in each iteration using steepest descent and conjugate direction methods. Expand
Incremental linear discriminant analysis for classification of data streams
TLDR
The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods. Expand
Incremental Kernel Principal Component Analysis
TLDR
The basis of the proposed solution lies in computing incremental linear PCA in the kernel induced feature space, and constructing reduced-set expansions to maintain constant update speed and memory usage. Expand
Novelty Detection in Learning Systems
Novelty detection is concerned with recognising inputs that differ in some way from those that are usually seen. It is a useful technique in cases where an important class of data isExpand
A review of novelty detection
TLDR
This review aims to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade. Expand
Efficient Kernel Discriminant Analysis via Spectral Regression
  • Deng Cai, X. He, Jiawei Han
  • Computer Science, Mathematics
  • Seventh IEEE International Conference on Data Mining (ICDM 2007)
  • 2007
TLDR
By using spectral graph analysis, SRKDA casts discriminant analysis into a regression framework which facilitates both efficient computation and the use of regularization techniques, which is a huge save of computational cost. Expand
Implementation of incremental linear discriminant analysis using singular value decomposition for face recognition
TLDR
In the proposed ILDA-SVD algorithm, it is proved that the approximation error is mathematically bounded, and the simulation results on Yale database show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate. Expand
...
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