Abstract
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.
Original language | English |
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Title of host publication | 2010 20th International Conference on Pattern Recognition (ICPR) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 4044-4047 |
Number of pages | 4 |
ISBN (Print) | 978-1-4244-7542-1 |
DOIs | |
Publication status | Published - 26 Aug 2010 |
Event | International Conference on Pattern Recognition (ICPR 2010), 20th - Istanbul, Turkey Duration: 23 Aug 2010 → 26 Aug 2010 |
Conference
Conference | International Conference on Pattern Recognition (ICPR 2010), 20th |
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Country/Territory | Turkey |
City | Istanbul |
Period | 23/08/10 → 26/08/10 |