Database Reference
In-Depth Information
• Making learning and generalization of models easier when our data has a very
large number of features (for example, when working with text, sound, images, or
video data, which tends to be very high-dimensional)
In this chapter, we will:
• Introduce the types of dimensionality reduction models available in MLlib
• Work with images of faces to extract features suitable for dimensionality reduc-
tion
• Train a dimensionality reduction model using MLlib
• Visualize and evaluate the results
• Perform parameter selection for our dimensionality reduction model
