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 951–975 of 3304 papers

TitleStatusHype
Classification of Cervical Cancer Dataset—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
Dimension reduction and redundancy removal through successive Schmidt decompositions—0
Stochastic neighborhood embedding and the gradient flow of relative entropy—0
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks—0
A Wasserstein perspective of Vanilla GANs—0
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Dimension Reduction for Efficient Data-Enabled Predictive Control—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Dimension Reduction for time series with Variational AutoEncoders—0
Classic machine learning methods—0
Dimension reduction in recurrent networks by canonicalization—0
Analyse en Composante Principale pour l'extraction des i-vecteurs en v\'erification du locuteur (Principal Component Analysis for i-vector extraction in speaker verification.) [in French]—0
Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM—0
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA—0
Dimension Reduction Using Active Manifolds—0
Dimension Reduction via Colour Refinement—0
Dimension reduction via score ratio matching—0
Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures—0
An Impossibility Theorem for Node Embedding—0
A Flexible Iterative Framework for Consensus Clustering—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