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 2201–2250 of 3304 papers

TitleStatusHype
Learning Clustered Representation for Complex Free Energy Landscapes—0
Learning Collective Behaviors from Observation—0
Learning Deep Representations By Distributed Random Samplings—0
Learning Deep Representation Without Parameter Inference for Nonlinear Dimensionality Reduction—0
Learning Densities Conditional on Many Interacting Features—0
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows—0
Learning Entropic Wasserstein Embeddings—0
Learning Environment Models with Continuous Stochastic Dynamics—0
An unsupervised approach to Geographical Knowledge Discovery using street level and street network images—0
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction—0
Learning Image Derived PDE-Phenotypes from fMRI Data—0
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems—0
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds—0
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions—0
Learning Isometric Embeddings of Road Networks using Multidimensional Scaling—0
Learning Locality-Constrained Collaborative Representation for Face Recognition—0
Learning low-dimensional dynamics from whole-brain data improves task capture—0
Learning Low-Dimensional Temporal Representations—0
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data—0
Learning Manifolds from Non-stationary Streaming Data—0
Learning Mixtures of Arbitrary Distributions over Large Discrete Domains—0
Learning Multiple Non-linear Sub-spaces Using K-RBMs—0
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains—0
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