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 2001–2025 of 3304 papers

TitleStatusHype
Coupled Control Systems: Periodic Orbit Generation with Application to Quadrupedal Locomotion—0
Gaze-Sensing LEDs for Head Mounted Displays—0
Unsupervised machine learning of quantum phase transitions using diffusion maps—0
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters—0
Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO—0
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
Multivariate Functional Regression via Nested Reduced-Rank Regularization—0
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors—0
Supervised Domain Adaptation using Graph EmbeddingCode1
Xtreaming: an incremental multidimensional projection technique and its application to streaming data—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
Spherical Principal Curves—0
Graphon Pooling in Graph Neural Networks—0
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection SystemCode0
Dimensionality reduction to maximize prediction generalization capabilityCode1
Statistical power for cluster analysisCode1
Supervised Dimensionality Reduction and Visualization using Centroid-encoder—0
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery—0
High-Dimensional Feature Selection for Genomic DatasetsCode0
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
Multivariate time-series modeling with generative neural networks—0
Reliable Distributed Clustering with Redundant Data Assignment—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