In many vision problems, the observed data lies in a nonlinear manifold in a high-dimensional space. This paper presents a generic modelling scheme to characterize the nonlinear structure of the manifold and to learn its multimodal distribution. Our approach represents the data as a linear combination of parameterized local components, where the statistics of the component parameterization describe the nonlinear structure of the manifold. The components are adaptively selected from the training data through a progressive density approximation procedure, which leads to the maximum likelihood estimate of the underlying density. We show results on both synthetic and real training sets, and demonstrate that the proposed scheme has the ability to reveal important structures of the data.
CITATION STYLE
Zhu, Y., Comaniciu, D., Schwartz, S., & Ramesh, V. (2002). Multimodal data representations with parameterized local structures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2350, pp. 173–189). Springer Verlag. https://doi.org/10.1007/3-540-47969-4_12
Mendeley helps you to discover research relevant for your work.