Manifold learning, a promised land or work in progress?

ABSTRACT In this paper, we report our experiments using a realworld image dataset to examine the effectiveness of Isomap, LLE and KPCA. The 1,897-image dataset we used consists of 14 image categories. We have used this dataset in several settings, both supervised and unsupervised, and have found it to be relatively “well behaved” (clusters do exist in a lower-dimensional space) compared to many other real-world datasets we have used. We did not use a “harder” database because all dimension-reduction methods would have failed miserably, and we would not be able to observe, identify, and explain the limitations of manifold learning. Tasks of image clustering and classification often deal with data of very high dimensions. To alleviate the dimensionality curse, several methods, such as Isomap, LLE and KPCA, have recently been proposed and applied to learn low-dimensional, non-linear embedded manifolds in high-dimensional spaces. Unfortunately, the scenarios in which these methods ...

• Type: Conference
• Year: 2005
• Where: ICMCS
• Authors: Mei-Chen Yeh, I-Hsiang Lee, Gang Wu, Yi Wu, Edward Y. Chang
• Keywords: 1, 897-image Dataset, ICMCS 2005, Realworld Image Dataset