NDLI: Analysis of linear and nonlinear dimensionality reduction methods for gender classification of face images

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Analysis of linear and nonlinear dimensionality reduction methods for gender classification of face images

Content Provider	Scilit
Author	Buchala, Samarasena Davey, Neil Gale, Tim M. Frank, Ray J.
Copyright Year	2005
Description	Journal: International Journal of Systems Science Data in many real world applications are high-dimensional and learning algorithms like neural networks may have problems in handling high-dimensional data. However, the 'intrinsic dimension (ID)' is often much less than the original dimension of the data. Here, we use fractal based methods to estimate the ID and show that a nonlinear projection method called curvilinear component analysis (CCA) can effectively reduce the original dimension to the ID. We apply this approach for dimensionality reduction of the face images data and use neural network classifiers for gender classification.
Related Links	https://core.ac.uk/download/pdf/1636858.pdf
Ending Page	942
Page Count	12
Starting Page	931
ISSN	00207721
e-ISSN	14645319
DOI	10.1080/00207720500381573
Journal	International Journal of Systems Science
Issue Number	14
Volume Number	36
Language	English
Publisher	Informa UK Limited
Publisher Date	2005-11-15
Access Restriction	Open
Subject Keyword	Journal: International Journal of Systems Science Information Systems Intrinsic Dimension Dimensionality Reduction Curvilinear Component Analysis Principal Component Analysis Gender Classification
Content Type	Text
Subject	Theoretical Computer Science Control and Systems Engineering Computer Science Applications

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