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 3126–3150 of 3304 papers

TitleStatusHype
Sketching out the Details: Sketch-based Image Retrieval using Convolutional Neural Networks with Multi-stage RegressionCode0
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection SystemCode0
ALPCAHUS: Subspace Clustering for Heteroscedastic DataCode0
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in ConnectomicsCode0
Functional Diffusion MapsCode0
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape DescriptorCode0
Online Recognition of Incomplete Gesture Data to Interface Collaborative RobotsCode0
ALPCAH: Subspace Learning for Sample-wise Heteroscedastic DataCode0
Function Preserving Projection for Scalable Exploration of High-Dimensional DataCode0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Funnels: Exact maximum likelihood with dimensionality reductionCode0
Fun with Flags: Robust Principal Directions via Flag ManifoldsCode0
A novel approach for Fair Principal Component Analysis based on eigendecompositionCode0
On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical PhenomenaCode0
Bubblewrap: Online tiling and real-time flow prediction on neural manifoldsCode0
Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signalsCode0
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern DataCode0
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value RegularizationCode0
Game-theoretic Objective Space PlanningCode0
An Information Theory-Based Feature Selection Framework for Big Data Under Apache SparkCode0
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problemsCode0
Asymptotics for Sketching in Least Squares RegressionCode0
On Projections to Linear SubspacesCode0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Linear Dimensionality Reduction: Survey, Insights, and GeneralizationsCode0
Show:102550
← PrevPage 126 of 133Next →
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