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 2951–2975 of 3304 papers

TitleStatusHype
Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape ModelsCode0
Neural Networks Perform Sufficient Dimension ReductionCode0
The Price of Fair PCA: One Extra DimensionCode0
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured SystemsCode0
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and SurveyCode0
Exact and Approximation Algorithms for Sparse PCACode0
NeuroDAVIS: A neural network model for data visualizationCode0
NeuroMapper: In-browser Visualizer for Neural Network TrainingCode0
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text SpatializationsCode0
Knowledge Base Index Compression via Dimensionality and Precision ReductionCode0
Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIsCode0
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High DimensionsCode0
q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor EmbeddingCode0
QUACK: Quantum Aligned Centroid KernelCode0
Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objectiveCode0
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying densityCode0
Deep generative LDACode0
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute ModelsCode0
Label Ranker: Self-Aware Preference for Classification Label Position in Visual Masked Self-Supervised Pre-Trained ModelCode0
A Perturbation-Based Kernel Approximation FrameworkCode0
A DEEP ADVERSARIAL LEARNING METHODOLOGY FOR DESIGNING MICROSTRUCTURAL MATERIAL SYSTEMSCode0
Language Model Training Paradigms for Clinical Feature EmbeddingsCode0
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding DistributionCode0
Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language ModelsCode0
Exploring Language Similarities with Dimensionality Reduction TechniqueCode0
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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