A Preprocessing Manifold Learning Strategy Based on t-Distributed Stochastic Neighbor Embedding.

Area of Science:

• Machine Learning
• Data Analysis
• Computational Statistics

Background:

• Manifold learning techniques like t-Distributed Stochastic Neighbor Embedding (t-SNE) are crucial for dimensionality reduction and visualizing high-dimensional data.
• Standard t-SNE can face challenges with data aggregation and computational complexity, impacting visualization effectiveness.
• The Kullback-Leibler divergence (KLD) is a key metric for evaluating the quality of probability distributions in embedding spaces.

Purpose of the Study:

• To significantly improve the manifold learning scheme of t-Distributed Stochastic Neighbor Embedding (t-SNE) through a novel preprocessing strategy.
• To enhance the separation of data clusters and maintain intra-cluster cohesion in high-dimensional datasets.
• To reduce computational and space complexity while improving visualization performance.

Main Methods:

• Introduction of a preprocessing strategy for t-SNE involving Laplacian eigenmaps for initial dimensionality reduction.
• Integration of the k-nearest-neighbor (KNN) algorithm within the preprocessing pipeline to refine data aggregation and visualization.
• Comparative performance analysis against standard t-SNE using the MNIST dataset.

Main Results:

• The proposed preprocessing strategy demonstrates a superior ability to aggregate data clusters and reduce Kullback-Leibler divergence (KLD) by approximately 30%.
• Enhanced visualization performance with improved separation between different data clusters and closer proximity for data points within the same cluster.
• A marginal increase in runtime complexity (1-2%) compared to standard t-SNE, indicating efficient scalability.

Conclusions:

• The novel preprocessing strategy significantly enhances t-SNE's effectiveness for high-dimensional data visualization and dimensionality reduction.
• Laplacian eigenmaps and KNN integration offer a robust approach to improve cluster separation and data representation fidelity.
• This method provides a valuable advancement for machine learning and data analysis applications requiring accurate data visualization.