The paper presents ENS-t-SNE, an algorithm that extends the t-SNE dimension reduction technique to create a 3D embedding that can capture multiple perspectives or subspaces of a high-dimensional dataset.
The key highlights are:
ENS-t-SNE generalizes the t-SNE cost function to optimize for multiple distance matrices simultaneously, each corresponding to a different perspective or subspace of the data.
The resulting 3D embedding allows the viewer to "walk around" and see different aspects of the data, with each 2D projection from the 3D view highlighting a different set of clusters or relationships.
This enables a more comprehensive understanding of the dataset compared to standard 2D projections, as the different perspectives are coherently linked in the 3D space.
Experiments on synthetic and real-world datasets demonstrate that ENS-t-SNE can effectively recover and visualize multiple types of clusters that are missed by standard dimension reduction techniques.
Quantitative evaluation shows that ENS-t-SNE outperforms the prior work on Multi-Perspective Simultaneous Embedding (MPSE) in preserving local neighborhoods and cluster structures in the 2D projections.
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