MLS-C01 Exploratory Data Analysis Practice Question
A machine learning team is working with a dataset containing high-dimensional sparse features, such as text data represented as bag-of-words. The team wants to reduce dimensionality while preserving the structure of the sparse matrix. Which technique is most appropriate for this scenario?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Truncated Singular Value Decomposition (SVD).
Truncated SVD (e.g., using sklearn's TruncatedSVD) is specifically designed for sparse matrices and efficiently reduces dimensionality while preserving the structure of the sparse matrix. Option A (t-SNE) is primarily for visualization and does not preserve the global structure well for dimensionality reduction. Option C (LDA) is a supervised method that requires labels and is not suitable for unsupervised dimensionality reduction. Option D (PCA using covariance matrix) requires a dense matrix and is computationally expensive for high-dimensional sparse data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
t-distributed Stochastic Neighbor Embedding (t-SNE).
Why it's wrong here
t-SNE is for visualization, not general reduction.
- ✓
Truncated Singular Value Decomposition (SVD).
Why this is correct
Truncated SVD works efficiently on sparse matrices.
- ✗
Linear Discriminant Analysis (LDA).
Why it's wrong here
LDA is supervised and requires labels.
- ✗
Principal Component Analysis (PCA) using the covariance matrix.
Why it's wrong here
PCA with covariance is not suitable for sparse data.
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