Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Rapid and brief communication: Two-dimensional FLD for face recognition
Pattern Recognition
Enhanced fisher discriminant criterion for image recognition
Pattern Recognition
Joint geometry and variability for image recognition
Neurocomputing
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
Global plus local: A complete framework for feature extraction and recognition
Pattern Recognition
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This paper presents a novel manifold learning method, namely two-dimensional supervised local similarity and diversity projection (2DSLSDP), for feature extraction. The proposed method defines two weighted adjacency graphs, namely similarity graph and diversity graph. The affinity matrix of similarity graph is determined by the spatial relationship between vertices of this graph, while affinity matrix of diversity graph is determined by the diversity information of vertices of its graph. Using these two graphs, the proposed method constructs local similarity scatter and diversity scatter, respectively. A concise feature extraction criterion is then raised via minimizing the ratio of the local similarity scatter to local diversity scatter. Thus, 2DSLSDP can well preserve not only the adjacency similarity structure, but also the diversity of data points, which is important for the classification. Experiments on the AR and UMIST databases show the effectiveness of the proposed method.