Name
Abstract | ||
|---|---|---|
We propose Merge Growing Neural Gas (MGNG) as a novel unsupervised growing neural network for time series analysis. MGNG combines the state-of-the-art recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons a priori and only constant parameters are used. In order to focus on frequent sequence patterns an entropy maximization strategy is utilized which controls the creation of new neurons. Experimental results demonstrate reduced time complexity compared to MNG while retaining similar accuracy in time series representation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02397-2_2 | WSOM |
Keywords | Field | DocType |
frequent sequence pattern,time series analysis,infinite time series,merge neural gas,neural gas,entropy maximization strategy,constant parameter,time series representation,time complexity,incremental unsupervised time series,self organization,time series,neural network | Time series,Pattern recognition,Computer science,Entropy maximization,A priori and a posteriori,Time delay neural network,Artificial intelligence,Artificial neural network,Time complexity,Neural gas,Recursion | Conference |
Volume | ISSN | Citations |
5629 | 0302-9743 | 9 |
PageRank | References | Authors |
0.65 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Andreakis | 1 | 9 | 0.65 |
| Nicolai V. Hoyningen-Huene | 2 | 12 | 1.12 |
| Michael Beetz | 3 | 3784 | 284.03 |