Manifold learning, A promised land or work in progress?

Mei Chen Yeh, I. Hsiang Lee, Gang Wu, Yi Wu, Edward Y. Chang

研究成果: 書貢獻/報告類型會議貢獻

13 引文 斯高帕斯(Scopus)

摘要

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 appear to be effective are very contrived. In this work, we empirically examine these methods on a realistic but not-so-difficult dataset. We discuss the promises and limitations of these dimensionreduction schemes.

原文英語
主出版物標題IEEE International Conference on Multimedia and Expo, ICME 2005
頁面1154-1157
頁數4
DOIs
出版狀態已發佈 - 2005 十二月 1
對外發佈Yes
事件IEEE International Conference on Multimedia and Expo, ICME 2005 - Amsterdam, 荷兰
持續時間: 2005 七月 62005 七月 8

出版系列

名字IEEE International Conference on Multimedia and Expo, ICME 2005
2005

其他

其他IEEE International Conference on Multimedia and Expo, ICME 2005
國家荷兰
城市Amsterdam
期間05/7/605/7/8

ASJC Scopus subject areas

  • Engineering(all)

引用此

Yeh, M. C., Lee, I. H., Wu, G., Wu, Y., & Chang, E. Y. (2005). Manifold learning, A promised land or work in progress?IEEE International Conference on Multimedia and Expo, ICME 2005 (頁 1154-1157). [1521631] (IEEE International Conference on Multimedia and Expo, ICME 2005; 卷 2005). https://doi.org/10.1109/ICME.2005.1521631