Name
Title | ||
|---|---|---|
Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification. |
Abstract | ||
|---|---|---|
Trajectory classification has been extensively investigated in recent years, however, the problems about automatically modeling unlabeled and incomplete trajectories are far from being solved. In this paper, we propose a Cluster-based Dictionary Learning (CDL) approach that firstly constructs an initial cluster-based dictionary by K-means clustering and incrementally updates by exploring the importance of the label consistency constraint and classification optimization. Finally, a multiple-category classifier for trajectory is obtained with Locality-constrained Sparse Reconstruction (LSR) that combines both sparsity and local adaptability for robust trajectory classification. Experimental results show that our approach outperforms several recent approaches. |
Year | Venue | Field |
|---|---|---|
2016 | ICASSP | Adaptability,Locality,Dictionary learning,K-SVD,Pattern recognition,Computer science,Artificial intelligence,Classifier (linguistics),Cluster analysis,Trajectory,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
21 | 2 | |