Image recognition with LPP mixtures

Authors:
SiBao Chen;Min Kong;Bin Luo
Affiliations:
Key Lab of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei, China;Key Lab of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei, China;Key Lab of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 6
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers

Neural Computation
PCA versus LDA

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition using LDA mixture model

Pattern Recognition Letters
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Locality preserving projections (LPP) can find an embedding that preserves local information and discriminates data well. However, only one projection matrix over the whole data is not enough to discriminate complex data. In this paper, we proposed locality preserving projections mixture models (LPP mixtures), where the set of all data were partitioned into several clusters and a projection matrix for each cluster was obtained. In each cluster, We performed LPP via QR-decomposition, which is efficient computationally in under-sampled situations. Its theoretical foundation was presented. Experiments on a synthetic data set and the Yale face database showed the superiority of LPP mixtures.