An efficient kernel discriminant analysis method
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …
Rapid and brief communication: An efficient kernel discriminant analysis method
J Lu, KN Plataniotis, AN Venetsanopoulos… - Pattern Recognition, 2005 - dl.acm.org
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …
[PDF][PDF] An efficient kernel discriminant analysis method
J Lu, KN Plataniotis, AN Venetsanopoulos… - Pattern …, 2005 - dsp.toronto.edu
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …
[PDF][PDF] An efficient kernel discriminant analysis method
J Lu, KN Plataniotis, AN Venetsanopoulos… - Pattern …, 2005 - academia.edu
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …
An efficient kernel discriminant analysis method
J Lu, KN Plataniotis, AN Venetsanopoulos, J Wang - Pattern Recognition, 2005 - infona.pl
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …
[PDF][PDF] An efficient kernel discriminant analysis method
J Lu, KN Plataniotis, AN Venetsanopoulos… - Pattern …, 2005 - comm.toronto.edu
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …
[PDF][PDF] An efficient kernel discriminant analysis method
J Lu, KN Plataniotis, AN Venetsanopoulos, J Wang - Pattern Recognition, 2005 - Citeseer
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …
An efficient kernel discriminant analysis method
J Lu, KN Plataniotis… - Pattern …, 2005 - ui.adsabs.harvard.edu
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …
[PDF][PDF] An efficient kernel discriminant analysis method
J Lu, KN Plataniotis, AN Venetsanopoulos… - Pattern …, 2005 - comm.toronto.edu
Small sample size and high computational complexity are two major problems encountered
when traditional kernel discriminant analysis methods are applied to high-dimensional …
when traditional kernel discriminant analysis methods are applied to high-dimensional …