SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2026–2050 of 3304 papers

TitleStatusHype
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach—0
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese NetworksCode0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
Backprojection for Training Feedforward Neural Networks in the Input and Feature SpacesCode0
DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approach—0
Weighted Fisher Discriminant Analysis in the Input and Feature SpacesCode0
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders—0
Company classification using machine learning—0
A new set of cluster driven composite development indicators—0
Gaze-Sensing LEDs for Head Mounted Displays—0
Coupled Control Systems: Periodic Orbit Generation with Application to Quadrupedal Locomotion—0
Unsupervised machine learning of quantum phase transitions using diffusion maps—0
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
Multivariate Functional Regression via Nested Reduced-Rank Regularization—0
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors—0
Xtreaming: an incremental multidimensional projection technique and its application to streaming data—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
Spherical Principal Curves—0
Graphon Pooling in Graph Neural Networks—0
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection SystemCode0
Supervised Dimensionality Reduction and Visualization using Centroid-encoder—0
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery—0
High-Dimensional Feature Selection for Genomic DatasetsCode0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
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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