GELM

An unofficial python implementation of the discriminative graph regularized Extreme Learning Machine (GELM) proposed by Yong Peng et al. [1], with sklearn compatibility.

Warning Please note this is not an official implementation, nor has been tested on the datasets used in the original studies. Due to different libraries and hyperparameters used in the implementation (and potentially implementation errors), there might be differences in the performance of this model to the ones as described in the papers. Please always examine the source code, make your own changes if necessary, and describe the actual implementation if you are using this model for an academic study. And please raise an issue if you found any implementation error in my code, thank you!

Introduction

I coded this GELM model based on papers by Yong Peng et al. [1] and Wei-Long Zheng et al. [2]. Please cite [1] (and [2]) when using this model. This model has been coded to include the sklearn estimator API, and could be used as a standard sklearn classifier.

I used this model to perform my electroencephalogram (EEG) analyses, hence the default hyperparameters were tuned toward my specific usage. Please always do some hyper parameter tunings before using the model on your dataset.

Requirements

This model was coded and tested on Python 3.9 with the following libraries and versions (minor differences in versions should not affect the model outcomes):

numpy >= 1.21.6
scikit-learn >= 1.1.3

Examples

See "GELM_example.ipynb".

>>> import numpy as np
>>> from sklearn.datasets import load_digits
>>> from sklearn.model_selection import cross_val_score, GridSearchCV

>>> from GELM import GELMClassifier

>>> X, y = load_digits(return_X_y=True)
>>> print(X.shape)
(1797, 64)

>>> scores = cross_val_score(GELMClassifier(l1=2**0, l2=2**10, random_state=42), X, y)
>>> print(np.mean(scores))
0.9560538532961932
>>> print(scores)
[0.95555556 0.92777778 0.96935933 0.96935933 0.95821727]

References

[1] Y. Peng, S. Wang, X. Long, and B. L. Lu, “Discriminative graph regularized extreme learning machine and its application to face recognition,” Neurocomputing, vol. 149, no. Part A, pp. 340–353, Feb. 2015, doi: 10.1016/J.NEUCOM.2013.12.065.

[2] W. L. Zheng, J. Y. Zhu, and B. L. Lu, “Identifying stable patterns over time for emotion recognition from eeg,” IEEE Trans. Affect. Comput., vol. 10, no. 3, pp. 417–429, 2019, doi: 10.1109/TAFFC.2017.2712143.