What is PCA?

Principal Component Analysis (PCA) is a data reduction technique used widely across a variety of fields. It is an unsupervised algorithm that seeks to discover the underlying structure of a data set by finding the principal components that are responsible for most of the variance in the data. The method works by selecting a set of variables (principal components) that summarise the information in the data set with the most efficiency.

PCA is the simplest of the true “dimensionality reduction” algorithms. In practice, it is widely used as a tool for visualising data, reducing the number of variables used in experiments, and pre-processing of data for predictive modeling.

Using PCA for Data Reduction

One of the main uses of PCA is to reduce the dimensionality of the data. In other words, it is used to reduce the amount of ‘noise’ in the data set and to reveal the underlying structure of the dataset. This can be done by eliminating highly correlated variables, or by reducing the number of dimensions that are used to capture the variance in the data set.

For example, if a data set consists of 10 columns it can be reduced to 6 columns using a PCA algorithm. The 6 columns that are selected are typically the ones that explain most of the variance in the data set. This process can be repeated until all the variance in the data set is explained by a sufficient number of features.

Benefits of PCA

One of the main advantages of using PCA is that it can reduce the complexity of the data set and highlight the underlying structure of the data. This allows the user to identify important features, clusters, and relationships between the variables in the data set.

Furthermore, it allows the user to reduce the amount of ‘noise’ in the data set, which can improve the accuracy of predictive models that are built on the data. Additionally, it reduces the number of variables that are used, which can simplify the interpretability of the models, and improve the efficiency of the model building process.

Conclusion

In conclusion, PCA is a data reduction technique that is widely used in a variety of fields. Its main purpose is to reduce the amount of ‘noise’ in a data set and to reveal the underlying structure of the data. It can also reduce the number of variables used in experiments, and enable better interpretability of models.