Explore the practical aspects of t-SNE, focusing on key hyperparameters and the interpretability of results. Sharpen your understanding of perplexity, learning rate, initialization, and how to make sense of t-SNE plots in dimensionality reduction tasks.
In t-SNE, which hyperparameter primarily controls the balance between considering local and global data structure, such as when summarizing clusters in a dataset?
Explanation: Perplexity is a key t-SNE hyperparameter that influences how local or global the embedding is, affecting how the algorithm balances focus on small versus large clusters. Learning rate principally adjusts the speed of optimization, not structure balance. Momentum affects updates but not neighborhood emphasis. Batch size is not a standard t-SNE hyperparameter. Therefore, perplexity is the correct answer.
What is most likely to happen if the learning rate is set too low while running t-SNE on a visualization task?
Explanation: A too-low learning rate slows down the gradient descent process, causing t-SNE to converge very slowly or get stuck early. Overshooting minima typically results from a too-high learning rate. Initialization refers to starting values and is unrelated to learning rate. Perplexity is a different parameter entirely, so only slow convergence is accurate.
When analyzing a t-SNE plot, how should the distances between points in the 2D map be interpreted?
Explanation: In t-SNE, points that are close together in the low-dimensional map tend to be close in the original space, reflecting high-dimensional similarities. However, exact distances are not preserved due to dimensionality reduction, making option two wrong. Clusters can vary in size, and distant points simply indicate low similarity, not absolute dissimilarity, so the third and fourth options don't accurately reflect t-SNE's properties.
Which initialization method can help improve reproducibility when running t-SNE multiple times on the same data?
Explanation: Setting a fixed random seed during t-SNE initialization ensures that the same results are produced for repeated runs, as it controls the randomness in the starting layout. Higher perplexity changes neighborhood size, not reproducibility. Batch size and iteration count also do not address initialization randomness. Thus, using a fixed random seed is the correct method.
Which statement best describes a limitation of using t-SNE for exploratory data analysis?
Explanation: t-SNE excels at preserving local relationships but does not reliably maintain global distances; distant points may not represent true high-dimensional distances. Perplexity is adjustable, making option two incorrect. t-SNE is data-agnostic, working with any kind of data, not just images. The method's random initialization means plots are rarely identical across runs, so only the first option is correct.
If you select a very high perplexity relative to your dataset size in t-SNE, what issue are you most likely to encounter?
Explanation: Using a high perplexity causes the algorithm to focus on larger neighborhoods, which can blur or merge distinct local clusters because it acts more globally. Noise in plots is not a direct result of perplexity. Outlier abundance is more related to local structure, and perplexity does not dictate learning rate selection, so the first option is the correct effect.
Are the numerical axis values (e.g., X and Y) in a t-SNE plot meaningful for interpreting the original dataset features?
Explanation: In t-SNE, axes do not correspond to any specific feature or preserve original units; only the relative placement of points contains useful information. Each run may even rotate or flip the axes. Treating axes as features or units is incorrect, and saying the axes are random noise ignores their function in conveying relationships.
When your t-SNE plot shows points heavily overlapping in dense clusters, which parameter is most helpful to adjust to reduce overplotting?
Explanation: Adjusting perplexity changes the neighborhood size t-SNE considers, making it possible to better separate dense clusters and reduce overplotting. Learning rate primarily affects convergence, not clustering clarity. Axis label size is part of visualization rather than the algorithm. Batch size is not a standard t-SNE parameter, making perplexity the best choice.
Which practical strategy helps ensure more stable convergence of t-SNE when visualizing a complex dataset?
Explanation: Increasing the number of iterations allows t-SNE to gradually optimize the layout and reach a stable solution, especially for complex data. Setting perplexity to zero is not meaningful and can break the algorithm. Random initialization should be controlled, not ignored, to reduce run-to-run variation. A very low learning rate hampers convergence, so only increasing iterations is the correct choice.
When using t-SNE, what can you infer if you observe distinct, well-separated clusters in the 2D plot?
Explanation: Distinct clusters in t-SNE generally indicate that sets of data points are similar in the original high-dimensional space. However, clusters do not always directly correspond to class labels unless the data is labeled and clusters align. Coordinates do not directly map to feature values, and the physical size of the plot is a display choice, not a data property.