Review Article
A Review of Feature Extraction Software for Microarray Gene Expression Data
Table 6
A summary of PLS software.
| Number | Software | Author/year | Language | Features |
| 1 | PLS Discriminant Analysis |
Barker and Rayens [24] | C/C++, Visual Basic | PLS for discriminant analysis |
| 2 | Least Squares–PLS |
Jørgensen et al. [25] | R | Implementation combining PLS and ordinary least squares |
| 3 | Powered PLS Discriminant Analysis | Liland and Indahl [26] | R | Extraction of information for multivariate classification problems |
| 4 | Penalized PLS |
Krmer et al. (2008) [27] | R | Extension of PLS regression using penalization technique |
| 5 | SlimPLS | Gutkin et al. [22] | R | Multivariate feature extraction method which incorporates feature dependencies |
| 6 | Sparse PLS Discriminant Analysis, Sparse Generalized PLS | Chung and Keles [28] | R | Sparse version techniques employing feature extraction and dimension reduction simultaneously |
| 7 | PLS Degrees of Freedom | Kramer and Sugiyama [29] | R | Using an unbiased estimation of the degrees of freedom for PLS regression |
| 8 | Surrogate Variable Analysis PLS |
Chakraborty and Datta [30] | R | Extraction of the informative features with hidden confounders which are unaccounted for |
| 9 | PLS Path Modelling | Sanchez and Trinchera [31] | R | A multivariate feature extraction analysis technique based on the cause-effect relationships of the unobserved and observed features |
| 10 | PLS Regression for Generalized Linear Models |
Bertrand et al. (2013) [32] | R | PLS regression is used to extract the predictive features from the generalized linear models |
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