Title :
Image analysis with regularized Laplacian eigenmaps
Author :
Tompkins, Frank ; Wolfe, Patrick J.
Author_Institution :
Sch. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA, USA
Abstract :
Many classes of image data span a low dimensional nonlinear space embedded in the natural high dimensional image space. We adopt and generalize a recently proposed dimensionality reduction method for computing approximate regularized Laplacian eigenmaps on large data sets and examine for the first time its application in a variety of image analysis examples. These experiments demonstrate the potential of regularized Laplacian eigenmaps in developing new learning algorithms and improving performance of existing systems.
Keywords :
Laplace transforms; eigenvalues and eigenfunctions; image processing; dimensionality reduction method; image analysis; learning algorithms; natural high dimensional image space; regularized Laplacian eigenmaps; Algorithm design and analysis; Head; Kernel; Laplace equations; Linear approximation; Training; Dimensionality reduction; Laplacian eigenmaps; image analysis; unsupervised learning;
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2010.5651856
