Name
Abstract | ||
|---|---|---|
In this paper we elaborate on a kernel extension to tensor-based data analysis. The proposed ideas find applications in supervised learning problems where input data have a natural 2-way representation, such as images or multivariate time series. Our approach aims at relaxing linearity of standard tensor-based analysis while still exploiting the structural information embodied in the input data. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15822-3_7 | ICANN (2) |
Keywords | Field | DocType |
multivariate time series,input data,infinite dimensional,tensor-based data analysis,standard tensor-based analysis,2-way tensors,supervised learning problem,structural information,proposed idea,2-way representation,kernel-based learning,kernel extension,data analysis,supervised learning | Kernel (linear algebra),Semi-supervised learning,Pattern recognition,Tensor,Computer science,Multivariate statistics,Empirical risk minimization,Linearity,Supervised learning,Matrix norm,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | ISBN |
6353 | 0302-9743 | 3-642-15821-8 |
Citations | PageRank | References |
1 | 0.37 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Signoretto | 1 | 155 | 9.10 |
| Lieven De Lathauwer | 2 | 3002 | 226.72 |
| Johan A K Suykens | 3 | 2346 | 241.14 |