Sparse Principal Component Analysis (sparse PCA) represents a significant advance in the field of dimensionality reduction for high-dimensional data. Unlike conventional Principal Component Analysis ...

Tuesday, October 28: Often researchers are faced with data in very high dimensions (e.g. too many predictors for a regression model), or must come up with a rule to classify data in pre-determined ...

A good way to see where this article is headed is to take a look at the screen shot of a demo program shown in Figure 1. The demo sets up a dummy dataset of six items: [ 5.1 3.5 1.4 0.2] [ 5.4 3.9 1.7 ...

Transforming a dataset into one with fewer columns is more complicated than it might seem, explains Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step machine learning tutorial.

Multivariate analysis is commonly used when we have more than one outcome variables for each observation. For instance, a survey of American adults’ physical and mental health might measure each ...