Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Description Using Scale-Space Edge Pixel Directions Histogram
SMAP '07 Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Linear Neighborhood Propagation and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Circuits and Systems for Video Technology
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In the paper, we present an improved approach based on Semisupervised Discriminant Analysis (SDA), called semi-supervised local discriminant embedding (SLDE), for reducing the dimensionality of the feature space. We take the manifold structure into account and try to learn a subspace in which the Euclidean distances can better reflect class structure of the images. The weight matrix and the scatter matrices in SDA are improved to make efficient use of both labeled and unlabeled images. After being embedded into a low-dimensional subspace, the similar images maintain their intrinsic neighbor relations, whereas the dissimilarity neighboring images no longer stick to one another. Experiments have been carried out to validate our approach.