NCLDR: Nearest-Neighbor Contrastive Learning with Dual Correlation Loss for Dimensionality Reduction

This publication has 21 references indexed in Scilit:

• Feature extraction framework based on contrastive learning with adaptive positive and negative samples
  Neural Networks, 2022
• Self-supervised contrastive learning for integrative single cell RNA-seq data analysis
  Briefings in Bioinformatics, 2022
• Momentum Contrast for Unsupervised Visual Representation Learning
  2020
• The art of using t-SNE for single-cell transcriptomics
  Nature Communications, 2019
• Dimensionality reduction for visualizing single-cell data using UMAP
  Nature Biotechnology, 2018
• Deep Clustering for Unsupervised Learning of Visual Features
  2018
• Deep Residual Learning for Image Recognition
  2016
• A survey on unsupervised outlier detection in high‐dimensional numerical data
  Statistical Analysis and Data Mining: An ASA Data Science Journal, 2012
• Random Forests
  Machine Learning, 2001
• LIII. On lines and planes of closest fit to systems of points in space
  Journal of Computers in Education, 1901