A New Distance Measure for Model-Based Sequence Clustering
Dario Garcia-Garcia, Emilio Parrado-Hernandez and Fernando Diaz-de-MAria
IEEE Transactions on Pattern Analysis and Machine Intelligence
We review the existing alternatives for defining model-based distances for clustering sequences and propose a new one based on the Kullback-Leibler divergence. This distance is shown to be especially useful in combination with spectral clustering. For improved performance in real-world scenarios, a model selection scheme is also proposed.