A machine learning engineer is using Uniform Manifold Approximation and Projection (UMAP) to visualize a high-dimensional biological dataset. The initial visualization shows the data separated into several small, distinct clusters. However, based on domain knowledge, the engineer expects to see a more continuous structure with connections between these clusters. Which of the following hyperparameter adjustments is the most effective approach to encourage UMAP to capture more of the dataset's global structure?
Increase the value of the n_neighbors parameter.
Decrease the value of the min_dist parameter.
Apply Principal Component Analysis (PCA) with a higher number of components before running UMAP.
Change the metric parameter from 'euclidean' to 'cosine'.
The correct answer is to increase the value of the n_neighbors parameter. The n_neighbors hyperparameter in UMAP controls the balance between preserving local and global structure in the data. A small n_neighbors value forces the algorithm to focus on very local structure, which can result in a fragmented visualization with many small clusters. By increasing n_neighbors, the algorithm considers a larger neighborhood for each point, allowing it to learn and preserve the broader, global structure of the data manifold, which would help connect the seemingly separate clusters.
Decreasing the min_dist parameter is incorrect. This parameter controls how tightly points are packed in the low-dimensional embedding. A smaller min_dist value results in denser, more compact clusters, which would likely enhance the separation between clusters rather than revealing their connections.
Applying PCA with more components before UMAP is an incorrect approach to this specific problem. While PCA can be used as a preprocessing step, the core issue described is about the balance of local versus global structure within the UMAP algorithm itself, which is directly controlled by n_neighbors, not the dimensionality of the input to UMAP.
Changing the distance metric is also incorrect. While the choice of metric is important for defining distance in the high-dimensional space, it is not the primary parameter for controlling the trade-off between local and global structure preservation. The n_neighbors parameter is the most direct way to address the issue of an overly local focus.
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What is the role of the `n_neighbors` parameter in UMAP?
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Why does decreasing the `min_dist` parameter lead to more compact clusters?
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How does changing the distance metric, like switching from 'euclidean' to 'cosine', affect UMAP results?