Local and Global Structures Preserving Projection

Authors:
Hao Cheng;Kien A. Hua;Khanh Vu
Affiliations:
-;-;-
Venue:
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Year:
2007

Citing 0
Cited 5

Semi-supervised dimensionality reduction in image feature space

Proceedings of the 2008 ACM symposium on Applied computing
Leveraging user query log: toward improving image data clustering

CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
SubSpace Projection: A unified framework for a class of partition-based dimension reduction techniques

Information Sciences: an International Journal
An automatic feature generation approach to multiple instance learning and its applications to image databases

Multimedia Tools and Applications
Local and global structure preserving based feature selection

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose Local and Global Structures Preserving Projection (LGSPP), which is to find a small set of projection directions so as to properly preserve the local and global structures for a given set of data. Specifically, for each point in the dataset, its local neighborhood is extracted as well as a set of sampled points far away from this point, which characterize the global structure. The embedding minimizes the distances of the points in each local neighborhood while dispersing them far apart from their corresponding remote points. In this way, the local-global relationships between data points are well kept.