Name
Abstract | ||
|---|---|---|
Partial least squares (PLS) has been widely applied to process scientific data sets as an effective dimension reduction technique. The main way to determine the number of dimensions extracted by PLS is by using the cross validation method, but its computation load is heavy. Researchers presented fixing the number at three, but intuitively it's not suitable for all data sets. Based on the intrinsic connection between PLS and the structure of data sets, two novel algorithms are proposed to determine the number of extracted principal components, keeping the valuable information while excluding the trivial. With the merits of variety with different data sets and easy implementation, both algorithms exhibit better performance than the previous works on nine real world data sets. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patrec.2011.11.009 | Pattern Recognition Letters |
Keywords | Field | DocType |
easy implementation,real world data set,different data set,effective dimension reduction technique,scientific data set,cross validation method,algorithms exhibit,better performance,model selection,computation load,dimension reduction,partial least squares | Effective dimension,Data set,Dimensionality reduction,Partial least squares regression,Artificial intelligence,Computation,Pattern recognition,Model selection,Algorithm,Statistics,Cross-validation,Principal component analysis,Mathematics | Journal |
Volume | Issue | ISSN |
33 | 5 | 0167-8655 |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guo-Zheng Li | 1 | 368 | 42.62 |
| Rui-Wei Zhao | 2 | 12 | 1.99 |
| Haini Qu | 3 | 31 | 2.03 |
| Mingyu You | 4 | 160 | 16.22 |