Face and Expression Recognition

Faithful Supervised Dimensionality Reduction for Biomedical Data via Decision Geometry

The paper introduces DG-UMAP, a supervised dimensionality reduction method that improves class structure preservation in biomedical data while maintaining local geometry.

Dimensionality Reduction with Strong Global Structure Preservation

This paper introduces two objectives to improve dimensionality reduction by preserving both global and local structure, achieving better performance on biomedical datasets.

ADM: Adaptive Graph Diffusion for Meta-Dimension Reduction

ADM combines multiple dimension reduction techniques using graph diffusion to better capture complex data structures and sample heterogeneity.

openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding

openTSNE is a Python library for t-SNE dimensionality reduction that is faster than existing implementations and supports mapping new data to existing embeddings.

Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets

This paper introduces DeCOr-MDS, a robust MDS method that detects and corrects orthogonal outliers to improve embedding quality in biological datasets.

A Spectral Method for Assessing and Combining Multiple Data Visualizations

The paper introduces a spectral method to evaluate and combine multiple data visualizations, improving the quality of the consensus visualization by leveraging eigenscores.

Enhancing t-SNE Performance for Biological Sequencing Data through Kernel Selection

This paper evaluates different kernels for t-SNE to improve its performance on biological sequencing data, finding that the cosine similarity kernel provides the best results for SARS-CoV-2 spike protein sequences.