Explains SNE, t-SNE, and their variants for manifold learning.
problem Dimensionality reduction and manifold learning.
method Probabilistic approach using Gaussian and Student-t distributions.
result Out-of-sample extension and acceleration methods for t-SNE.
ct-SNE extracts hidden structure from labeled data.
problem Insufficient 2D visualization of high-dimensional data.
method Conditional t-SNE discounts prior information from labels.
result Extracts complementary structure not captured by t-SNE alone.
FIt-SNE accelerates t-SNE for large datasets.
problem Slow computation of t-SNE for large datasets.
method Interpolation-based t-SNE (FIt-SNE) using FFT and oocPCA.
result Significant acceleration of t-SNE computation for large datasets.
Tree-SNE combines t-SNE and hierarchical clustering for data visualization.
problem Data visualization and clustering in complex datasets.
method Stacked one-dimensional t-SNE embeddings and alpha-clustering.
result Effective hierarchical clustering and visualization of various datasets.
t-SNE with Cauchy kernel shows finer cluster structure.
problem Crowding problem in t-SNE visualizations.
method Developed an efficient implementation of t-SNE with a heavy-tailed t-distribution kernel. result Fine cluster structure revealed with ν<1. t-SNE loses important features in data visualization.
problem t-SNE's loss of important features in data visualization.
method Established mathematical framework to understand t-SNE's loss in different scenarios.
result t-SNE loses important features of data in various scenarios.
A new method, tree-SNE, solves the scale problem in t-SNE.
problem Clustering and visualizing high-dimensional data, especially MNIST digits.
method Revisits t-SNE idea to create a 2+1 dimensional embedding with a scale parameter.
result The optimal embedding depends continuously on the scale parameter for all initial conditions.
Optimizes t-SNE for high-dimensional data with random projections.
problem High computational cost of t-SNE for high-dimensional data.
method Use random projections to reduce high-dimensional data to a few dimensions, then apply t-SNE.
result Random projections preserve clustering while significantly reducing t-SNE runtime.
t-SNE algorithm's points remain bounded under gradient flow.
problem Understanding the boundedness of t-SNE points.
method Gradient flow of t-SNE with KL divergence, examining weak convergence assumptions.
result Points generated by t-SNE remain bounded under gradient flow.
S-multi-SNE combines t-SNE with labelling data for better multi-view data visualisation and classification.
problem Visualizing and classifying multi-view data with limited labels.
method S-multi-SNE extends multi-SNE by incorporating labelling data as an additional view for semi-supervised classification and visualization.
result Including labelling information significantly improves the projection of samples and classification performance.
Revised ct-SNE improves t-SNE for visualizing data beyond labels.
problem ct-SNE fails in realistic settings where data is well clustered over labels.
method Conditioning high-dimensional similarities, storing nearest neighbors separately, and using speedups.
result Improved embedding quality and scalability.
Theoretical analysis of t-SNE for visualizing clustered data.
problem Understanding t-SNE for visualizing high-dimensional clustered data.
method Gradient descent approach and power iterations based on graph Laplacian.
result Asymptotic equivalence and limiting behavior of t-SNE's early exaggeration stage.
t-SNE fails to reveal clusters even in well-clusterable data.
problem t-SNE's failure to show clusters in well-clusterable data.
method Empirical evidence and theoretical analysis of t-SNE's limitations.
result t-SNE may hide clustering patterns even with strong signals in the data.
Proposes a model selection method for t-SNE perplexity.
problem Manual tuning of t-SNE perplexity is difficult and requires expertise.
method Develops a model selection objective for t-SNE perplexity that minimizes extra computation.
result Perplexity settings found by the approach are consistent with human expert preferences.
Interactive tool for better understanding t-SNE projections.
problem Interpreting t-SNE projections can be challenging and misleading.
method Interactive visualization tool with different views.
result Improves understanding of t-SNE and its results.
SNE successfully separates well-separated clusters in high dimensions.
problem No theoretical results are known for SNE and its variants.
method Stochastic Neighbor Embedding and variants.
result SNE successfully separates well-separated clusters in high dimensions.
t-SNE-CUDA accelerates visualization of large datasets on GPUs.
problem Efficiently visualizing high-dimensional datasets and models.
method GPU-accelerated t-SNE implementation.
result Significant speedups (50-700x) on large datasets.
This paper improves t-SNE using Isolation kernel for better data representation and efficiency.
problem Misrepresentation of data structures and high computational cost in t-SNE.
method Replacing Gaussian kernel with Isolation kernel in t-SNE.
result Isolation kernel improves t-SNE's accuracy and efficiency without sacrificing quality.
Paper studies t-SNE convergence with generalized kernels.
problem Understanding convergence of t-SNE with generalized kernels.
method Concrete formulation of generalized kernels, proving convergence to an equilibrium distribution.
result t-SNE converges to an equilibrium distribution under certain conditions for generalized kernels.
Mathematical analysis of SNE and t-SNE for dimension reduction.
problem Optimal mapping of high-dimensional data to low dimensions.
method Gradient flow of relative entropy to minimize the distance between points.
result The diameter of the evolving sets remains bounded for SNE but may blow up for t-SNE.
Proposes a parametric t-SNE without perplexity tuning.
problem Non-parametric t-SNE's perplexity parameter limits DR quality.
method Multi-scale parametric t-SNE with deep neural network.
result Produces reliable embeddings with competitive neighborhood preservation.
Unified view of SOMs and SNE from a common framework.
problem Comparing and understanding SOMs and SNE.
method Unified mathematical framework, quantitative comparison on datasets.
result SOMs and SNE can be derived from a common framework.
ARS visualization improves t-SNE dynamics with tunable attraction and repulsion.
problem Improve data visualization techniques for complex data sets.
method ARS framework based on t-SNE dynamics with normalized interactions and tunable kernels.
result ARS visualization provides better control over cluster tightness and spacing.
Proposes MR-SNE for multimodal data visualization.
problem Visualizing data from multiple domains with relations across them.
method Extends t-SNE to compute augmented relations and jointly embed them in a low-dimensional space.
result Demonstrates promising performance in visualizing Flickr and Animal with Attributes 2 datasets.
This paper proves t-SNE can recover well-separated clusters, improving visualization and embedding quality.
problem The lack of mathematical foundations and inner workings of t-SNE.
method Proves t-SNE's ability to recover well-separated clusters, using early exaggeration phase and rigorous analysis.
result t-SNE in the early exaggeration phase can be rigorously analyzed and provides novel ways to set parameters.
This paper finds a linear relationship between t-SNE perplexity and data set size.
problem Choosing the right perplexity for t-SNE embeddings.
method Analyzed the relationship between perplexity and data set size.
result Embeddings remain structurally consistent when perplexity is adjusted accordingly.
The paper presents an O(N log N)-implementation of t-SNE -- an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N^2). The new implementation uses vantage-point trees to compute sparse pairwise similarities between the input data object…
A new method embeds sparse stochastic graphs into low dimensions.
problem Embedding large, sparse, stochastic graphs into low-dimensional spaces.
method Spaceland Embedding (SG-t-SNE) inspired by t-SNE, leveraging modern computing techniques.
result Effective embedding results on synthetic and real-world graphs.
Parametric t-SNE improves generalization for streaming data.
problem Training neural networks for t-SNE objective function fails due to gradient exploding.
method Applied gradient clipping to solve gradient exploding problem.
result Parametric t-SNE achieves quality compatible with non-parametric t-SNE while generalizing to new data.
This work extends t-SNE to f-divergences for better visualization of data.
problem Visualizing high-dimensional data with improved accuracy and structure capture.
method Extending t-SNE to f-divergences, analytically and empirically evaluating different types of latent structure.
result Different f-divergences perform better for different types of latent structure.
New methods for visualizing multi-view data improve clustering accuracy.
problem Visualizing multi-view data for better understanding of samples.
method Extensions of manifold learning methods (t-SNE, LLE, ISOMAP) for multi-view data.
result Improved clustering accuracy of multi-view data.
Paper uses t-SNE to classify China's Internet finance risks.
problem Systemic risk characteristics of China's Internet finance during macroeconomic shocks.
method t-SNE machine learning algorithm for data mining and risk classification.
result Identified peak and thick-tail characteristics of Internet financial systemic risk.
Paper interprets UMAP and t-SNE as probabilistic MAP inference.
problem Understanding and interpreting UMAP and t-SNE.
method Interprets UMAP and t-SNE as MAP inference methods corresponding to a probabilistic model of the graph Laplacian.
result Shows UMAP and t-SNE can be understood as probabilistic inference methods.
Wasserstein t-SNE embeds hierarchical datasets considering within-unit distributions.
problem Exploring hierarchical datasets where units are compared based on means of sample distributions.
method Uses Wasserstein distance metric for 2D embeddings of units, approximating Gaussian distributions for efficiency.
result Demonstrates effective embedding of hierarchical datasets, uncovering meaningful structure.
This work proves the continuum limit of t-SNE for data visualization.
problem Understanding the theoretical basis of t-SNE from a continuum limit perspective.
method Proving the Kullback-Leibler divergence consistency as no∞ for t-SNE. result The continuum variational problem involving non-convex gradient regularization and penalty on probability density function magnitude.
This paper evaluates t-SNE and MDS for reducing dimensions in datasets and classifying them with KNN, ENN, and SVM.
problem Reducing dimensions in datasets for better classification performance.
method t-SNE and MDS applied to nine datasets, followed by KNN, ENN, and SVM classification.
result Performance comparison of t-SNE and MDS with KNN, ENN, and SVM.
Neighbor embeddings balance attraction and repulsion to visualize data.
problem Visualizing high-dimensional datasets with trade-offs between continuous and discrete structures.
method Neighbor embeddings combine attractive and repulsive forces to visualize data.
result Changing the exaggeration parameter in t-SNE yields a spectrum of embeddings with a trade-off between continuous and discrete structures.
New approach combines PCA and t-sne for better data analysis.
problem Multiscale complexity in high-dimensional data.
method Multiscale joint characterization using PCA and t-sne.
result Joint characterization detects signals not seen by PCA or t-sne alone.
GraphTSNE visualizes graph data by integrating graph structure and node features.
problem Lack of suitable visualization techniques for graph-structured data.
method GraphTSNE combines t-SNE with graph convolutional networks to visualize graph data.
result GraphTSNE produces better visualizations of graph data compared to existing methods.
Study characterizes cryospheric spectral feature space using joint PC+t-SNE approach.
problem Characterize cryospheric spectral feature space for remote sensing applications.
method Compare and contrast two approaches for identifying feature space basis vectors via dimensionality reduction (PCA and t-SNE).
result Joint characterization reveals distinct continua and clusters of ice reflectance properties.
This paper uses t-SNE to visualize multi-objective electric machine optimization at various operating points.
problem Visualization of multi-objective electric machine optimization at multiple operating points is challenging.
method Utilizes t-distributed stochastic neighbor embedding (t-SNE) to visualize high-dimensional data.
result t-SNE provides better visualizations of electric machine design candidates and their performance.
GTSNE improves data visualization for high-dimensional data.
problem Visualizing high-dimensional data points in a 2D map.
method GTSNE is a variation of t-SNE that captures both local and macro structures.
result GTSNE produces better visualizations of high-dimensional data compared to other methods.
Study uses DNA methylation data to predict suicidal and non-suicidal deaths.
problem Predicting suicidal and non-suicidal deaths from DNA methylation data.
method Support Vector Machines (SVM) with dimensionality reduction using PCA and t-SNE.
result t-SNE outperforms PCA in reducing data dimensionality.
TriMap improves data visualization by preserving global structure better than existing methods.
problem Visualizing high-dimensional data with preserved global structure.
method TriMap uses triplet constraints for dimensionality reduction.
result TriMap outperforms other methods in terms of runtime and quality of embedding.
A new approach clusters data first, then embeds each cluster, improving transparency.
problem Visualizing data with latent clusters while preserving global geometry.
method First cluster, then embed each cluster, aligning the clusters.
result The approach is competitive with existing methods and more transparent.
Auto insurers improve risk assessment using t-SNE.
problem Accurate risk estimation for auto insurance policyholders.
method Combining neural network with t-SNE for dimensionality reduction.
result Visual representation of risk as a 2D surface, revealing high vs low risk policyholders.
p-SNE embeds Poisson count data into low dimensions preserving structure.
problem Embedding high-dimensional sparse Poisson data into a low-dimensional space.
method p-SNE (Poisson Stochastic Neighbor Embedding) using KL divergence and Hellinger distance.
result p-SNE recovers meaningful structure in real-world count datasets.
New RL algorithms find SNE in Markov games with myopic followers.
problem Finding SNE in Markov games with myopic followers.
method Optimistic and pessimistic variants of least-squares value iteration, incorporating function approximation.
result First provably efficient RL algorithms for SNEs in general-sum Markov games with myopic followers.