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 2176–2200 of 3304 papers

TitleStatusHype
Revealing computational mechanisms of retinal prediction via model reduction—0
Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila—0
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion—0
Imitation Learning from Pixel-Level Demonstrations by HashReward—0
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation ClassificationCode0
A Flexible Framework for Anomaly Detection via Dimensionality ReductionCode0
Quantized Fisher Discriminant AnalysisCode0
Solving Interpretable Kernel Dimension ReductionCode0
Restricted Minimum Error Entropy Criterion for Robust Classification—0
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion—0
Fusing Vector Space Models for Domain-Specific Applications—0
Latent Gaussian process with composite likelihoods and numerical quadrature—0
Progressive Disentanglement Using Relevant Factor VAE—0
Lifetime Ruin under High-watermark Fees and Drift Uncertainty—0
Mixture Probabilistic Principal Geodesic Analysis—0
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