A new proposal for locality preserving projection

Authors:
Gitam Shikkenawis;Suman K. Mitra
Affiliations:
Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat, India;Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat, India
Venue:
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Year:
2012

Citing 8
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Locality preserving indexing for document representation

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Incremental semi-supervised subspace learning for image retrieval

Proceedings of the 12th annual ACM international conference on Multimedia
Locality preserving clustering for image database

Proceedings of the 12th annual ACM international conference on Multimedia
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-LPP: A two-dimensional extension of locality preserving projections

Neurocomputing
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Image retrieval based on incremental subspace learning

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Locality Preserving Projection (LPP) is the recently proposed approach for dimensionality reduction to preserve the neighbourhood information. It is widely used for finding the intrinsic dimensionality of data. As LPP preserves the information about the nearest neighbours of data points, it may lead to misclassification in the overlapping regions of two or more classes. The conventional method works on a graph based technique where weights given to the edges are used to emphasize the local information. In this paper, we propose a new weighing scheme for the neighbourhood preserving graph which also gives importance to the data points that are at a moderate distance, in addition to the nearest points. This helps in resolving the ambiguity occurring in the overlapping regions. The proposal is tested on varying datasets.