Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal locality preserving indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Image retrieval based on incremental subspace learning
Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Palmprint recognition with improved two-dimensional locality preserving projections
Image and Vision Computing
Two-dimensional discriminant locality preserving projections for face recognition
Pattern Recognition Letters
2D-LPI: Two-Dimensional Locality Preserving Indexing
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Fast communication: Active energy image plus 2DLPP for gait recognition
Signal Processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Plant classification using leaf image based on 2D linear discriminant analysis
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Face recognition via two dimensional locality preserving projection in frequency domain
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
The analysis of parameters t and k of LPP on several famous face databases
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Supervised Discriminant Projection with Its Application to Face Recognition
Neural Processing Letters
A new proposal for locality preserving projection
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Two-Dimensional locality discriminant projection for plant leaf classification
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
On approaching 2D-FPCA technique to improve image representation in frequency domain
Proceedings of the Fourth Symposium on Information and Communication Technology
Multi-linear neighborhood preserving projection for face recognition
Pattern Recognition
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We consider the problem of locality preserving projections (LPP) in two-dimensional sense. Recently, LPP was proposed for dimensionality reduction, which can detect the intrinsic manifold structure of data and preserve the local information. As far as matrix data, such as images, are concerned, they are often vectorized for LPP algorithm to find the intrinsic manifold structure. While the dimension of matrix data is usually very high, LPP cannot be implemented because of the singularity of matrix. In this paper, we propose a method called two-dimensional locality preserving projections (2D-LPP) for image recognition, which is based directly on 2D image matrices rather than 1D vectors as conventional LPP does. From an algebraic procedure, we induce that 2D-LPP is related to two other linear projection methods, which are based directly on image matrix: 2D-PCA and 2D-LDA. 2D-PCA and 2D-LDA preserve the Euclidean structure of image space, while 2D-LPP finds an embedding that preserves local information and detects the intrinsic image manifold structure. To evaluate the performance of 2D-LPP, several experiments are conducted on the ORL face database, the Yale face database and a digit dataset. The high recognition rates and speed show that 2D-LPP achieves better performance than 2D-PCA and 2D-LDA. Experiments even show that conducting PCA after 2D-LPP achieves higher recognition than LPP at the same dimension of feature spaces.