SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2191–2200 of 3304 papers

TitleStatusHype
A study of semantic augmentation of word embeddings for extractive summarization—0
Estimation of a function of low local dimensionality by deep neural networks—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
Unsupervised Construction of Knowledge Graphs From Text and Code—0
Principal Component Analysis Using Structural Similarity Index for ImagesCode0
Locally Linear Image Structural Embedding for Image Structure Manifold LearningCode0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics—0
Locally Linear Embedding and fMRI feature selection in psychiatric classification—0
Tensor-Train Parameterization for Ultra Dimensionality Reduction—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