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 2821–2830 of 3304 papers

TitleStatusHype
High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts—0
Self-calibrating Neural Networks for Dimensionality Reduction—0
Non-Redundant Spectral Dimensionality Reduction—0
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity—0
Dimensionality Reduction of Massive Sparse Datasets Using Coresets—0
Large Margin Discriminant Dimensionality Reduction in Prediction Space—0
SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives—0
LSTM Autoencoders for Dialect Analysis—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Image-based localization using LSTMs for structured feature correlation—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