Machine Learning Algorithms: Unsupervised Learning & Training

That's right! With dimension reduction, the goal is to reduce the data into a more manageable set. Under principal component analysis, the algorithm identifies the highest correlation amongst all observations, which captures the most volatility in the dataset. From there, other groups are identified based upon lower correlations until the entire dataset is represented by groups.

Which of the following describes the dimension reduction method of principal component analysis?

Incorrect. That describes a clustering algorithm, not principal component analysis.

No. The data set may be all significantly correlated, so equal groups aren't going to help researchers identify core factors.

It works to reduce the data by correlation into different groups

It uses a $$K$$ algorithm to reduce the distance between a centroid

It works to reduce the data into equal groups based upon correlations