Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/39828
Title: Supervised manifold learning for image and video classification
Authors: Liu, Y
Liu, Y 
Chan, KCC 
Keywords: Image and video classification
L1-norm optimization
Manifold learning
Maximum distance embedding
Multilinear maximum distance embedding
Supervised learning
Issue Date: 2010
Source: MM '10 Proceedings of the International Conference on Multimedia, Firenze, Italy, Oct 25-29, 2010, p. 859-862 How to cite?
Abstract: This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize the distance preserving, we propose a novel algorithm called maximum distance embedding (MDE), which aims to maximize the distances between some particular pairs of data points, with the intention of flattening the local nonlinearity and keeping the discriminant information simultaneously in the embedded feature space. Moreover, MDE measures the dissimilarity between data points using L1-norm distance, which is more robust to outliers than widely used Frobenius norm distance. To adapt the nature tensor structure of image and video data, we further propose the multilinear MDE (M2DE). Experiments on various datasets demonstrate that both MDE and M2DE achieve impressive embedding results of image and video data for classification tasks.
URI: http://hdl.handle.net/10397/39828
ISBN: 978-1-60558-933-6
DOI: 10.1145/1873951.1874097
Appears in Collections:Conference Paper

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