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Unified formulation of linear discriminant analysis methods and optimal parameter selection

  • Published:2011-02 Issue:2 Volume:44 Page:307-319
  • ISSN:0031-3203
  • Container-title:Pattern Recognition
  • language:en
  • Short-container-title:Pattern Recognition

Author:

An Senjian,Liu Wanquan,Venkatesh Svetha,Yan Hong

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference43 articles.

1. The Elements of Statistical Learning;Hastie,2001

2. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection;Belhumeur;IEEE Trans. Pattern Anal. Mach. Intell.,1997

3. Solving the small sample size problem of LDA;Huang,2002

4. A two-stage linear discriminant analysis via QR-decomposition;Ye;IEEE Trans. Pattern Anal. Mach. Intell.,2005

5. Generalizing discriminant analysis using generalized singular value decomposition;Howland;IEEE Trans. Pattern Anal. Mach. Intell.,2004

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1. Eigenspectrum regularisation reverse neighbourhood discriminative learning;IET Computer Vision;2024-05-14

2. An Operation-Level Collaborative Filtering Approach Based on Logistic Function for Web Services Recommendation;2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI);2018-10

3. Inexact implementation using Krylov subspace methods for large scale exponential discriminant analysis with applications to high dimensionality reduction problems;Pattern Recognition;2017-06

4. Learning Regularized LDA by Clustering;IEEE Transactions on Neural Networks and Learning Systems;2014-12

5. ClubCF: A Clustering-Based Collaborative Filtering Approach for Big Data Application;IEEE Transactions on Emerging Topics in Computing;2014-09
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