Name
Abstract | ||
|---|---|---|
The Kernel-based orthogonal projections to latent structures (K-OPLS) model is a recent novel data analysis method for both regression and classification. Compared with the classical orthogonal projections to latent structures (OPLS), it utilizes the kernel Gram matrix as a replacement of descriptor matrix to use the partial least squares (PLS) model. This enables it can effectively improve predictive performance, considerably in such situations where strong non-linear relationships between descriptor and response variables while retaining the OPLS model framework. In this paper, we first introduce the K-OPLS model. And then, a load forecasting model based on K-OPLS is proposed. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/EMEIT.2011.6023132 | EMEIT |
Keywords | Field | DocType |
partial least square,orthogonal projections to latent structures,kernel pls,pls model,least squares approximations,k-opls,kernel-based orthogonal projections,partial least squares model,orthogonal signal correction,load forecasting,orthogonal projection,data analysis methods | Kernel (linear algebra),Regression,Matrix (mathematics),Control theory,Partial least squares regression,Algorithm,OPLS,Load forecasting,Artificial intelligence,Gramian matrix,Mathematics,Machine learning | Conference |
Volume | Issue | ISBN |
9 | null | 978-1-61284-087-1 |
Citations | PageRank | References |
1 | 0.44 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingcai Kong | 1 | 1 | 0.44 |
| Yanpeng Ma | 2 | 39 | 7.38 |