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2010
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A Bound on the Performance of LDA in Randomly Projected Data Spaces

12 years 9 months ago
A Bound on the Performance of LDA in Randomly Projected Data Spaces
We consider the problem of classification in nonadaptive dimensionality reduction. Specifically, we bound the increase in classification error of Fisher’s Linear Discriminant classifier resulting from randomly projecting the high dimensional data into a lower dimensional space and both learning the classifier and performing the classification in the projected space. Our bound is reasonably tight, and unlike existing bounds on learning from randomly projected data, it becomes tighter as the quantity of training data increases without requiring any sparsity structure from the data.
Robert John Durrant, Ata Kaban
Added 13 May 2010
Updated 13 May 2010
Type Conference
Year 2010
Where ICPR
Authors Robert John Durrant, Ata Kaban
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