SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1900 of 3304 papers

TitleStatusHype
Going Beyond T-SNE: Exposing whatlies in Text Embeddings—0
Towards Measuring Place Function Similarity at Fine Spatial Granularity with Trajectory Embedding—0
RRScell method for automated single-cell profiling of multiplexed immunofluorescence cancer tissue—0
Deep generative LDACode0
The Mathematical Foundations of Manifold Learning—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
Deep Manifold Transformation for Nonlinear Dimensionality Reduction—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Nested Grassmannians for Dimensionality Reduction with ApplicationsCode0
Improving the generalization of network based relative pose regression: dimension reduction as a regularizer—0
Geometry-Aware Hamiltonian Variational Auto-Encoder—0
Tensor Train Random Projection—0
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Auto-Encoding Variational Bayes for Inferring Topics and VisualizationCode0
Statistical guarantees for generative models without domination—0
Product Manifold LearningCode0
Sufficient dimension reduction for classification using principal optimal transport directionCode0
Prediction of daily maximum ozone levels using Lasso sparse modeling method—0
Interactive Latent Interpolation on MNIST DatasetCode0
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank EstimationCode0
Mycorrhiza: Genotype Assignment usingPhylogenetic NetworksCode0
Causal learning with sufficient statistics: an information bottleneck approach—0
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
Circular Coordinate Methods with Generalized Penalty Functions—0
Challenging Euclidean Topological AutoencodersCode0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images—0
Invertible Manifold Learning for Dimension ReductionCode0
Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins—0
Optimal High-order Tensor SVD via Tensor-Train Orthogonal IterationCode0
Parameter Optimization using high-dimensional Bayesian Optimization—0
Algorithms for Nonnegative Matrix Factorization with the Kullback-Leibler DivergenceCode0
Factorized Discriminant Analysis for Genetic Signatures of Neuronal PhenotypesCode0
Bayesian Feature Selection in Joint Quantile Time Series Analysis—0
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications—0
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information—0
Deep matrix factorizations—0
Facilitate the Parametric Dimension Reduction by Gradient Clipping—0
Evaluation of company investment value based on machine learning—0
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes—0
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning—0
Improved Dimensionality Reduction of various Datasets using Novel Multiplicative Factoring Principal Component Analysis (MPCA)—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and SurveyCode0
Deep Monocular Visual Odometry for Ground Vehicle—0
Compact Learning for Multi-Label Classification—0
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection—0
An Algorithm for Out-Of-Distribution Attack to Neural Network EncoderCode0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified