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 2041–2050 of 3304 papers

TitleStatusHype
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors—0
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
Spherical Principal Curves—0
Graphon Pooling in Graph Neural Networks—0
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection SystemCode0
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
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—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