Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Similarity Based Methods Introductio

The video tutorial covers similarity-based methods in feature selection, focusing on both supervised and unsupervised learning. It explains the construction and use of an affinity matrix, which is central to these methods. The tutorial also discusses the role of K-nearest neighbor graphs in defining similarity and introduces the concept of geodesic distance. Applications in feature extraction and future learning modules are briefly mentioned.

What is the primary focus of similarity-based methods in feature selection?

• To improve computational efficiency

• To reduce data redundancy

• To enhance data visualization

• To preserve data similarity

How is similarity defined in a supervised dataset?

• By the number of features

• By the size of the dataset

• By the similarity of class labels

• By the distance between data points

In unsupervised datasets, which metric is commonly used to define similarity?

• Euclidean distance

• Jaccard index

• Cosine similarity

• Manhattan distance

What is an affinity matrix?

• A matrix that optimizes algorithm performance

• A matrix that calculates data variance

• A matrix that represents pairwise similarity

• A matrix that stores feature importance

What is the role of K-nearest neighbor graphs in similarity-based methods?

• To construct an affinity matrix

• To reduce the number of features

• To calculate the shortest path between nodes

• To identify the most important features

Which algorithm is mentioned for finding the shortest distance in a graph?

• Prim's algorithm

• Kruskal's algorithm

• Dijkstra's algorithm

• Bellman-Ford algorithm

What is geodesic distance used for in the context of feature extraction?

• To optimize algorithm performance

• To measure data variance

• To determine the shortest path in a graph

• To calculate feature importance