Unsupervised Learning Flashcards

What is an autoencoder in the context of unsupervised learning?

Answer: A neural network trained to reconstruct its input through a bottleneck

An autoencoder compresses input to a lower-dimensional latent code (encoder) and then reconstructs the original input (decoder).

Which linkage criterion in hierarchical clustering tends to produce compact, spherical clusters?

Answer: Ward linkage

Ward linkage minimizes the total within-cluster variance at each merge step, producing compact and roughly spherical clusters.

What problem does ICA (Independent Component Analysis) solve that PCA does not?

Answer: Separating statistically independent non-Gaussian source signals

ICA finds a decomposition into statistically independent components, making it suitable for blind source separation tasks like separating audio signals.

What is a dendrogram used for in clustering?

Answer: Visualizing the hierarchy of cluster merges in agglomerative clustering

A dendrogram is a tree diagram showing the sequence of merges in hierarchical clustering, helping choose the number of clusters by cutting at a desired height.

Which assumption does K-Means make about cluster shapes?

Answer: Clusters are convex and isotropic (spherical)

K-Means uses Euclidean distance to assign points to the nearest centroid, implicitly assuming clusters are convex and roughly spherical.

In topic modeling with Latent Dirichlet Allocation (LDA), what are documents modeled as?

Answer: Mixtures of topics, where each topic is a distribution over words

LDA models each document as a mixture of latent topics, and each topic as a probability distribution over vocabulary words.

What does 'inertia' measure in K-Means clustering?

Answer: The sum of squared distances from each point to its assigned cluster centroid

Inertia (also called within-cluster sum of squares, WCSS) measures how tightly packed the clusters are; lower inertia means more compact clusters.