Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Exact indexing of dynamic time warping
Knowledge and Information Systems
Embedding of time series data by using dynamic time warping distances
Systems and Computers in Japan
Indexing Multidimensional Time-Series
The VLDB Journal — The International Journal on Very Large Data Bases
An effective double-bounded tree-connected Isomap algorithm for microarray data classification
Pattern Recognition Letters
Kernel ridge regression for out-of-sample mapping in supervised manifold learning
Expert Systems with Applications: An International Journal
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Experimental comparison of representation methods and distance measures for time series data
Data Mining and Knowledge Discovery
A comparative study of nonlinear manifold learning methods for cancer microarray data classification
Expert Systems with Applications: An International Journal
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Isometric feature mapping (Isomap) has proven high potential for nonlinear dimensionality reduction in a wide range of application domains. Isomap finds low-dimensional data projections by preserving global geometrical properties, which are expressed in terms of the Euclidean distances among points. In this paper we investigate the use of a recent variant of Isomap, called double-bounded tree-connected Isomap (dbt-Isomap), for dimensionality reduction in the context of time series gene expression classification. In order to deal with the projection of temporal sequences dbt-Isomap is combined with different lock-step and elastic measures which have been extensively proposed to evaluate time series similarity. These are represented by three $\mathcal L_p$-norms, dynamic time warping and the distance based on the longest common subsequence model. Computational experiments concerning the classification of two time series gene expression data sets showed the usefulness of dbt-Isomap for dimensionality reduction. Moreover, they highlighted the effectiveness of $\mathcal L_1$-norm which appeared as the best alternative to the Euclidean metric for time series gene expression embedding.