MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a high-dimensional dataset with 500 features. They want to visualize the data in 2D to check for clusters. They first apply PCA and get a 2D projection that shows no clear structure. They suspect that the data lies on a non-linear manifold. Which of the following techniques should they try next?
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
✓
Use t-distributed Stochastic Neighbor Embedding (t-SNE).
T-SNE is a non-linear dimensionality reduction technique specifically designed for visualization. Option A (ICA) is a linear method for separating independent components, not for capturing non-linear manifolds. Option B (LDA) is a supervised linear method that maximizes class separation, unsuitable for unsupervised non-linear structure. Option C (PCA with more components) remains linear, so adding components does not help with non-linear manifolds.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Independent Component Analysis (ICA).
Why it's wrong here
Independent Component Analysis (ICA) is a linear transformation that separates mixed signals into independent sources. It is not designed for visualizing non-linear manifolds.
- ✗
Use Linear Discriminant Analysis (LDA).
Why it's wrong here
Linear Discriminant Analysis (LDA) is a supervised technique that finds linear combinations to separate classes. It does not capture non-linear structures and requires labels.
- ✗
Apply PCA again with more components.
Why it's wrong here
PCA is a linear dimensionality reduction method. Applying it with more components still yields a linear projection, which cannot capture non-linear manifolds.
- ✓
Use t-distributed Stochastic Neighbor Embedding (t-SNE).
Why this is correct
t-SNE is a non-linear technique that preserves local structure and is widely used for visualizing high-dimensional data in 2D or 3D, making it ideal for detecting clusters in non-linear manifolds.
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