Efficient Classification for Large-scale Problems by Multiple LDA Subspaces

Martina Uray, Peter M. Roth, Horst Bischof · International Conference on Computer Vision Theory and Applications · 2009

Summary

The main idea is to split the original data into multiple sub-sets and to compute a single LDA space for each sub-set, which means the separability in the obtained subspaces is increased and the overall classification power is improved.

Type: article
Published: 2009-01-01
Cited by: 11
References: 17
Access: Open access
DOI: https://doi.org/10.5220/0001754802990306
OpenAlex: https://openalex.org/W56744618
Semantic Scholar: https://api.semanticscholar.org/CorpusID:5430796

Keywords

Discriminative model, Linear subspace, Computer science, Weighting, Computational complexity theory

References

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Efficient Classification for Large-scale Problems by Multiple LDA Subspaces

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