Hello everyone,
I'm new to using PARAFAC for decomposing EEMs data. I validated my results on three models: the first with three components, the second with four components, and the third with five components.
My question is: how do I choose the best model????
Thanks in advance
Choosing the optimal number of components in a PARAFAC model can be crucial. Let's explore some strategies to help you decide:
1. Explained Variance: Look at the explained variance for each model. This metric indicates how well the model captures the variability in your data. Generally, a higher explained variance is desirable. You can compare the explained variance across your three models and choose the one with the highest value.
2. Cross-Validation: Perform cross-validation on your models. Split your data into training and validation sets, fit the models, and evaluate their performance on the validation set. The model with the best performance (e.g., lowest error) might be the most suitable.
3. Physical Interpretability: Consider the physical interpretability of the components. Do the extracted features make sense in the context of your EEM data? Sometimes, a model with fewer components that align well with known chemical or physical processes is preferable.
4. Occam's Razor: Apply the principle of Occam's Razor: choose the simplest model that adequately explains your data. In other words, favor a model with fewer components if it performs similarly to more complex ones.
5. BIC or AIC: Use the Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC). These criteria penalize models with more parameters. Lower BIC or AIC values indicate better model fit.
6. Visual Inspection: Plot the reconstructed EEMs for each model and compare them to the original data. Visual inspection can reveal patterns and discrepancies. Choose the model that visually captures the features you expect.
Remember that there's no one-size-fits-all answer. Consider combining these approaches and consult domain experts if available. Ultimately, the best model depends on your analysis's specific context and goals.
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