Advantages and Disadvantages of Cross Decomposition

There are many advantages of using cross decomposition but with the advantages we must also consider the drawbacks that might come with the positives. Here are some of the advantages and disadvantages of using cross decomposition.

Advantages

• Handling Multicollinearity: Cross Decomposition is a great tool to use if the predictor dataset is filled with correlated data features. Collinearity is one of the main factors to consider in order to find an efficient model.
• Dimensionality Reduction: Cross Decomposition reduces the original dataset into set of latent variables which are less dimensional than the original dataset but captures the insights of the original dataset pretty well.
• Multivariate Relationship: Cross Decomposition is suitable for complex datasets with multiple sets of variables efficiently.
• Robustness to Imperfect Data: Cross Decomposition methods are well suited when we are dealing with data that is either missing some values or it contains noise or outliers. They can capture patterns even in the presence of missing data.

Disadvantages

• Overfitting: In situations where the features are greater ibn amount than the observed data the PLS model may get prone to overfitting, therefore, we must carefully consider proper model validation.
• Assumptions: These methods are based on the assumptions that there is a linear relationship between data, in case of non linear relationships cross decomposition might not be the best choice.
• Algorithm Sensitivity: The performance of cross decomposition algorithms can be sensitive to hyperparameters such as number of components. Selection of proper number of components could be done through cross validation but it increases the complexity of the model.
• Computational Complexity: As the number of dimensions, the features or target variables increases the algorithms might get computationally intense.

Usually, in real-world datasets, some of the features of the data are highly correlated with each other. Applying normal regression methods to highly correlated data is not an effective way to analyze such data, since multicollinearity makes the estimates highly sensitive to any change in the model. In this article, we will be diving deep into Cross Decomposition which will help us understand the optimal solutions to problems like multicollinearity in the data.