Question

In the context of face recognition using the Olivetti Faces dataset, which algorithm is commonly used for dimensionality reduction before applying classification techniques?

a) Linear Discriminant Analysis (LDA)
b) Support Vector Machines (SVM)
c) k-Nearest Neighbors (kNN)
d) Principal Component Analysis (PCA)

Answer 1

Principal Component Analysis (PCA) is commonly used for dimensionality reduction before applying classification techniques in face recognition using the Olivetti Faces dataset.

Step-by-step explanation:

In the context of face recognition using the Olivetti Faces dataset, the commonly used algorithm for dimensionality reduction before applying classification techniques is Principal Component Analysis (PCA).

PCA is a technique that transforms high-dimensional data into a lower-dimensional representation by finding the linear combinations of the original variables that retain the most variance in the data. In the case of face recognition, PCA can be used to extract the most important facial features or patterns from the images.

After applying PCA, the reduced-dimensional data can be fed into various classification techniques like Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), or k-Nearest Neighbors (kNN) for further analysis and recognition.