Tara N. Sainath, Dimitri Kanevsky, et al.
ICASSP 2007
We consider the problem of deriving compressed perceptual representation of multivariate time series and using it for efficient indexing and similarity search. Our algorithm is based on the identification of perceptual skeletons in multidimensional space and the use of these "simplifications" in similarity measurements. We illustrate the performance of the algorithm in a financial modeling application. Our results indicate that the skeleton representation outperforms the traditional approaches and is robust enough to be used even with the simplest distance metrics. ©2007 IEEE.
Tara N. Sainath, Dimitri Kanevsky, et al.
ICASSP 2007
Aleksandra Mojsilović, Emina Soljanin
IEEE International Symposium on Information Theory - Proceedings
Zhenbo Zhu, Qing Wang, et al.
ICASSP 2007
Vadim Sheinin, Da-Ke He
ICASSP 2007