TriMap: Large-scale Dimensionality Reduction Using Triplets

1 October 2019

Papers citing "TriMap: Large-scale Dimensionality Reduction Using Triplets"

44 / 44 papers shown

Title
IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservation Zihao Chen Wenyong Wang Jiachen Yang Yu Xiang 34 0 0 07 May 2025
The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction Mohammad Tariqul Islam Jason W. Fleischer 241 0 0 12 Mar 2025
Preserving clusters and correlations: a dimensionality reduction method for exceptionally high global structure preservation Jacob Gildenblat Jens Pahnke 211 1 0 10 Mar 2025
Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction Mattéo Clémot Julie Digne Julien Tierny 227 0 0 27 Feb 2025
Navigating the Effect of Parametrization for Dimensionality Reduction Haiyang Huang Yingfan Wang Cynthia Rudin 81 1 0 24 Nov 2024
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation Koshi Watanabe Keisuke Maeda Takahiro Ogawa Miki Haseyama 206 0 0 22 Oct 2024
Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning Isaac Ray Alexei Skurikhin 124 0 0 05 Sep 2024
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions Luca Reichmann David Hägele Daniel Weiskopf 201 0 0 07 Aug 2024
Outlier Detection in Large Radiological Datasets using UMAP Mohammad Tariqul Islam Jason W. Fleischer 56 1 0 31 Jul 2024
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning Shay Deutsch Lionel Yelibi Alex Tong Lin Arjun Ravi Kannan 56 1 0 04 Jun 2024
CBMAP: Clustering-based manifold approximation and projection for dimensionality reduction Berat Dogan 38 0 0 27 Apr 2024
Curvature Augmented Manifold Embedding and Learning Yongming Liu MedIm 123 2 0 21 Mar 2024
Scalable manifold learning by uniform landmark sampling and constrained locally linear embedding D. Peng Zhipeng Gui Wenzhang Wei Huayi Wu 40 1 0 02 Jan 2024
k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis Shashank Kotyan Tatsuya Ueda Danilo Vasconcellos Vargas 32 1 0 07 Dec 2023
Calibrating dimension reduction hyperparameters in the presence of noise Justin Lin Julia Fukuyama 34 1 0 05 Dec 2023
A ripple in time: a discontinuity in American history Alexander Kolpakov Igor Rivin AI4TS 11 0 0 02 Dec 2023
Efficiently Visualizing Large Graphs Xinyu Li Yao Xiao Yuchen Zhou 16 0 0 17 Oct 2023
Cluster Exploration using Informative Manifold Projections Stavros Gerolymatos Xenophon Evangelopoulos V. Gusev John Y. Goulermas 19 0 0 26 Sep 2023
Supervised Stochastic Neighbor Embedding Using Contrastive Learning Yi Zhang SSL 19 0 0 15 Sep 2023
GroupEnc: encoder with group loss for global structure preservation David Novak S. V. Gassen Yvan Saeys DRL 14 1 0 06 Sep 2023
Relating tSNE and UMAP to Classical Dimensionality Reduction Andrew Draganov Simon Dohn FAtt 32 4 0 20 Jun 2023
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets Tillmann Ohm M. Sola Andres Karjus Maximilian Schich 27 6 0 11 May 2023
Force-Directed Graph Layouts Revisited: A New Force Based on the T-Distribution Fahai Zhong Mingliang Xue Jian Zhang Fan Zhang Rui Ban Oliver Deussen Yunhai Wang 18 8 0 05 Mar 2023
InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images Konstantin Kobs M. Steininger Andreas Hotho VLM 18 3 0 23 Nov 2022
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection Yang Yang Hongjian Sun Jialei Gong Di Yu FAtt 34 2 0 17 Nov 2022
Unsupervised visualization of image datasets using contrastive learning Jan Boehm Philipp Berens D. Kobak SSL 26 15 0 18 Oct 2022
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation Ehsan Amid Rohan Anil Christopher Fifty Manfred K. Warmuth 28 2 0 15 Sep 2022
From $t$ -SNE to UMAP with contrastive learning Sebastian Damrich Jan Niklas Böhm Fred Hamprecht D. Kobak SSL 48 21 0 03 Jun 2022
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction M. Sarfraz Marios Koulakis C. Seibold Rainer Stiefelhagen 23 19 0 24 Mar 2022
Learning from Randomly Initialized Neural Network Features Ehsan Amid Rohan Anil W. Kotłowski Manfred K. Warmuth MLT 27 14 0 13 Feb 2022
Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM Adam Šmelko Sona Molnárová Miroslav Kratochvíl A. Koladiya J. Musil Martin Kruliš J. Vondrášek 21 0 0 03 Jan 2022
TLDR: Twin Learning for Dimensionality Reduction Yannis Kalantidis Carlos Lassance Jon Almazán Diane Larlus SSL 27 10 0 18 Oct 2021
Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures Y. Bartal Ora Nova Fandina Kasper Green Larsen 13 0 0 14 Jul 2021
An Analytical Survey on Recent Trends in High Dimensional Data Visualization Alex B. Kiefer Md. Khaledur Rahman AI4TS 14 2 0 05 Jul 2021
WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labels I. Karmanov F. G. Zanjani S. Merlin I. Kadampot Daniel Dijkman 27 14 0 31 May 2021
On UMAP's true loss function Sebastian Damrich Fred Hamprecht 29 39 0 26 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 653 0 20 Mar 2021
Where is your place, Visual Place Recognition? Sourav Garg Tobias Fischer Michael Milford 39 113 0 11 Mar 2021
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization Yingfan Wang Haiyang Huang Cynthia Rudin Yaron Shaposhnik 174 305 0 08 Dec 2020
Parametric UMAP embeddings for representation and semi-supervised learning Tim Sainburg Leland McInnes T. Gentner 31 217 0 27 Sep 2020
Bio-inspired Structure Identification in Language Embeddings Hongwei Zhou Zhou Oskar Elek P. Anand A. Forbes 11 1 0 05 Sep 2020
Attraction-Repulsion Spectrum in Neighbor Embeddings Jan Niklas Böhm Philipp Berens D. Kobak 34 53 0 17 Jul 2020
A Metric Learning Reality Check Kevin Musgrave Serge J. Belongie Ser-Nam Lim 59 475 0 18 Mar 2020
Self Organizing Nebulous Growths for Robust and Incremental Data Visualization Damith A. Senanayake Wei Wang S. Naik Saman K. Halgamuge AI4TS 13 16 0 09 Dec 2019