MS-DS Master of Data science Unsupervised Machine Learning Models 4

Question 1

In Non-negative Matrix Factorization (NMF), what constraint separates it from standard matrix factorization methods?

Accepted Answer

All elements in the factor matrices must be non-negative, enabling parts-based representations

Answer

NMF constrains all factor values to be ≥ 0, which leads to additive, parts-based decompositions (e.g., facial parts instead of eigenfaces).

Question 2

Which distance metric is most appropriate when comparing high-dimensional sparse vectors such as TF-IDF document representations?

Accepted Answer

Cosine similarity

Answer

Cosine similarity measures the angle between vectors and is invariant to document length, making it ideal for sparse high-dimensional text data.

Question 3

What is the primary purpose of the encoder in a Variational Autoencoder (VAE) compared to a standard autoencoder?

Accepted Answer

The VAE encoder outputs parameters of a probability distribution rather than a fixed latent vector

Answer

The VAE encoder predicts a mean and variance, enabling sampling from a learned latent distribution and smooth interpolation in latent space.

Question 4

In spectral clustering, what is the role of the graph Laplacian?

Accepted Answer

It encodes pairwise connectivity, and its eigenvectors reveal the cluster structure of the data

Answer

The eigenvectors of the graph Laplacian capture the connectivity structure of the similarity graph, allowing k-means to be applied in the spectral embedding space.

Question 5

Which criterion is commonly used to select the optimal number of topics in LDA?

Accepted Answer

Perplexity on a held-out test corpus

Answer

LDA perplexity measures how well the trained model predicts unseen documents; lower perplexity on held-out data indicates a better model fit.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Unsupervised Machine Learning Models 4