Abstract
In this paper, instead of the ordinary manifold assumption, we introduced the bundle manifold assumption that imagines data points lie on a bundle manifold. Under this assumption, we proposed an unsupervised algorithm, named as Bundle Manifold Embedding (BME), to embed the bundle manifold into low dimensional space. In BME, we construct two neighborhood graphs that one is used to model the global metric structure in local neighborhood and the other is used to provide the information of subtle structure, and then apply the spectral graph method to obtain the low-dimensional embedding. Incorporating some prior information, it is possible to find the subtle structures on bundle manifold in an unsupervised manner. Experiments conducted on benchmark datasets demonstrated the feasibility of the proposed BME algorithm, and the difference compared with ISOMAP, LLE and Laplacian Eigenmaps.
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Li, Cg., Guo, J., Zhang, Hg. (2010). Learning Bundle Manifold by Double Neighborhood Graphs. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_31
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DOI: https://doi.org/10.1007/978-3-642-12297-2_31
Publisher Name: Springer, Berlin, Heidelberg
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