View and style-independent action manifolds for human activity recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Probabilistic feature extraction from multivariate time series using spatio-temporal constraints
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Model-based recognition of human actions by trajectory matching in phase spaces
Image and Vision Computing
OBST-based segmentation approach to financial time series
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach, temporal information is intrinsic to the objective function, which produces description of low dimensional spaces with time coherence between data points. Since the proposed scheme also includes bidirectional mapping between data and embedded spaces and automatic tuning of key parameters, it offers the same benefits as mapping-based approaches. Experiments on a couple of computer vision applications demonstrate the superiority of the new approach to other dimensionality reduction method in term of accuracy. Moreover, its lower computational cost and generalisation abilities suggest it is scalable to larger datasets.