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 2226–2250 of 3304 papers

TitleStatusHype
Learning Nonautonomous Systems via Dynamic Mode Decomposition—0
Learning Optimal Deep Projection of ^18F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes—0
Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins—0
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
Learning Restricted Boltzmann Machines via Influence Maximization—0
Learning Semantics and Selectional Preference of Adjective-Noun Pairs—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
Learning Stochastic Dynamical Systems with Structured Noise—0
Learning Stochastic Representations of Physical Systems—0
Learning Treatment Representations for Downstream Instrumental Variable Regression—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction—0
Learning Word Meta-Embeddings—0
Lee and Seung (2000)'s Algorithms for Non-negative Matrix Factorization: A Supplementary Proof Guide—0
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks—0
Let the Tree Decide: FABART A Non-Parametric Factor Model—0
Multi-Dimensional Scaling on Groups—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting—0
License Plate Recognition with Compressive Sensing Based Feature Extraction—0
LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval—0
Lifespan tree of brain anatomy: diagnostic values for motor and cognitive neurodegenerative diseases—0
Lifetime Ruin under High-watermark Fees and Drift Uncertainty—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