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 1826–1850 of 3304 papers

TitleStatusHype
Probabilistic low-rank matrix completion on finite alphabets—0
Probabilistic partition of unity networks for high-dimensional regression problems—0
Probabilistic Principal Geodesic Analysis—0
Probabilistic Relational PCA—0
Probability distributions for analog-to-target distances—0
Probing Criticality in Quantum Spin Chains with Neural Networks—0
Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States—0
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation—0
Progressive Disentanglement Using Relevant Factor VAE—0
Progressive Monitoring of Generative Model Training Evolution—0
Progressive Rock Music Classification—0
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections—0
Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition—0
Projection Pursuit with Applications to scRNA Sequencing Data—0
Property-driven State-Space Coarsening for Continuous Time Markov Chains—0
Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation—0
Prototype Discovery using Quality-Diversity—0
Improving Covariance-Regularized Discriminant Analysis for EHR-based Predictive Analytics of Diseases—0
Pseudo-Non-Linear Data Augmentation via Energy Minimization—0
PSF field learning based on Optimal Transport Distances—0
Puzzle Imaging: Using Large-scale Dimensionality Reduction Algorithms for Localization—0
QEBA: Query-Efficient Boundary-Based Blackbox Attack—0
Quantifying Context Overlap for Training Word Embeddings—0
Quantifying the Conceptual Error in Dimensionality Reduction—0
Quantization Algorithms for Random Fourier Features—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