Name
Title | ||
|---|---|---|
Time Series Prediction For Kernel-Based Adaptive Filters Using Variable Bandwidth, Adaptive Learning-Rate, And Dimensionality Reduction |
Abstract | ||
|---|---|---|
Kernel-based adaptive filters are sequential learning algorithms, operating on reproducing kernel Hilbert spaces. Their learning performance is susceptible to the selection of appropriate values for kernel bandwidth and learning-rate parameters. Additionally, as these algorithms train the model using a sequence of input vectors, their computation scales with the number of samples. We propose a framework that addresses the previous open challenges of kernel-based adaptive filters. In contrast to similar methods, our proposal sequentially optimizes the bandwidth and learning-rate parameters using stochastic gradient algorithms that maximize the correntropy function. To remove redundant samples, a sparsification approach based on dimensionality reduction is introduced. The framework is validated on both synthetic and real-world data sets. Results show that our proposal converges to relatively low values of mean-square-error while provides stable solutions in real-world applications. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683117 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Sequential learning, Adaptive learning-rate, Kernel adaptive filters, Correntropy | Kernel (linear algebra),Hilbert space,Dimensionality reduction,Pattern recognition,Computer science,Kernel Bandwidth,Bandwidth (signal processing),Adaptive filter,Artificial intelligence,Sequence learning,Computation | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Garcia-Vega | 1 | 0 | 0.34 |
| E. A. Leon-Gomez | 2 | 0 | 0.34 |
| G. Castellanos-Dominguez | 3 | 2 | 2.09 |