.png){kind=icon}
Return
State-space dynamics distance for clustering sequential data
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2010.11.018.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a novel similarity measure for clustering sequential data. We first construct a common state space by training a single probabilistic model with all the sequences in order to get a unified representation for the dataset. Then, distances are obtained attending to the transition matrices induced by each sequence in that state space. This approach solves some of the usual overfitting and scalability issues of the existing semi-parametric techniques that rely on training a model for each sequence. Empirical studies on both synthetic and real-world datasets illustrate the advantages of the proposed similarity measure for clustering sequences. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Sequential data
Clustering
Hidden Markov models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
A TUTORIAL ON HIDDEN MARKOV-MODELS AND SELECTED APPLICATIONS IN SPEECH RECOGNITION.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
ShareSaveShareSaveTinnitus Retraining Therapy (TRT) as a Method for Treatment of Tinnitus and Hyperacusis Patients
PREAI
ShareSaveMechanical characterization of neural electrodes based on PDMS-parylene C-PDMS sandwiched system
PREAI
ShareSaveShareSaveShareSaveSecretory patterns of LH and FSH during development and hypothalamic and hypophysial characteristics following development of steroid-induced ovarian follicular cysts in dairy cattleReproductionOAAI
ReproductionIF0
OAAIShareSave
View more