Machine Learning Unsupervised Learning 5

Question 1

Which problem can arise when using K-Means with very high-dimensional data?

Accepted Answer

The curse of dimensionality makes Euclidean distances less meaningful

Answer

In high dimensions, distances between points become increasingly similar (concentration of measure), making it hard for K-Means to distinguish meaningful clusters.

Question 2

What is 'spectral clustering' and what advantage does it have?

Accepted Answer

It uses eigenvectors of a similarity graph Laplacian to cluster data of non-convex shapes

Answer

Spectral clustering constructs a similarity graph, computes its Laplacian eigenvectors, then clusters the low-dimensional embedding, allowing non-convex cluster detection.

Question 3

In Self-Organizing Maps (SOMs), what is the 'neighborhood function' responsible for?

Accepted Answer

Updating weights of neurons near the winning neuron to preserve topological structure

Answer

The neighborhood function ensures that neurons near the winning (best-matching) unit are also updated, causing the map to preserve topological relationships of the input data.

Question 4

What is 'co-clustering' (biclustering) in unsupervised learning?

Accepted Answer

Simultaneously clustering both rows and columns of a data matrix

Answer

Biclustering finds subsets of rows and columns in a matrix that exhibit coherent patterns, commonly used in gene expression analysis.

Question 5

Why is random initialization in K-Means a concern, and how does K-Means++ address it?

Accepted Answer

Random init can lead to poor local minima; K-Means++ spreads initial centroids to improve convergence

Answer

K-Means++ selects initial centroids with probability proportional to their distance from already-chosen centroids, reducing the chance of converging to a bad local minimum.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Unsupervised Learning 5