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 2201–2225 of 3304 papers

TitleStatusHype
Null Space Analysis for Class-Specific Discriminant Learning—0
A Critical Note on the Evaluation of Clustering Algorithms—0
Zero-Shot Feature Selection via Transferring Supervised Knowledge—0
GridDehazeNet: Attention-Based Multi-Scale Network for Image DehazingCode0
Visualizing the PHATE of Neural NetworksCode0
Improving Channel Charting with Representation-Constrained Autoencoders—0
Online Detection of Sparse Changes in High-Dimensional Data Streams Using Tailored ProjectionsCode0
Neuroscience-inspired online unsupervised learning algorithms—0
Likelihood Contribution based Multi-scale Architecture for Generative Flows—0
Effective Dimensionality Reduction for Word EmbeddingsCode0
Machine learning in APOGEE: Identification of stellar populations through chemical abundances—0
Autoencoding with a Learning Classifier System: Initial Results—0
Interpretability Beyond Classification Output: Semantic Bottleneck Networks—0
Completing partial recipes using item-based collaborative filtering to recommend ingredientsCode0
Performance-Complexity Tradeoffs in Greedy Weak Submodular Maximization with Random Sampling—0
High Dimensional Bayesian Optimization via Supervised Dimension ReductionCode0
Word Sense Disambiguation using Diffusion Kernel PCA—0
Compressed Subspace Learning Based on Canonical Angle Preserving Property—0
Multiscale Principle of Relevant Information for Hyperspectral Image ClassificationCode0
Improving the Projection of Global Structures in Data through Spanning Trees—0
The Perfect Marriage and Much More: Combining Dimension Reduction, Distance Measures and Covariance—0
A Light weight and Hybrid Deep Learning Model based Online Signature Verification—0
GP-VAE: Deep Probabilistic Time Series ImputationCode0
Visualization of Emergency Department Clinical Data for Interpretable Patient PhenotypingCode0
Generalized Principal Component Analysis—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