SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2801–2825 of 3304 papers

TitleStatusHype
Joint Embedding of GraphsCode0
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
Applying Ricci Flow to High Dimensional Manifold Learning—0
L^3-SVMs: Landmarks-based Linear Local Support Vectors Machines—0
A description length approach to determining the number of k-means clusters—0
Semi-supervised Learning based on Distributionally Robust Optimization—0
Feasibility of Principal Component Analysis in hand gesture recognition system—0
Visual response properties of MSTd emerge from a sparse population code—0
Exemplar-Centered Supervised Shallow Parametric Data Embedding—0
Maximally Correlated Principal Component Analysis—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
RIPML: A Restricted Isometry Property based Approach to Multilabel Learning—0
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing DataCode0
Stochastic Neighbor Embedding separates well-separated clusters—0
Toward the automated analysis of complex diseases in genome-wide association studies using genetic programmingCode0
Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning—0
Representation of big data by dimension reduction—0
Computational Techniques in Multispectral Image Processing: Application to the Syriac Galen Palimpsest—0
Faster Discovery of Faster System Configurations with Spectral Learning—0
Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps—0
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe—0
Self-Taught Convolutional Neural Networks for Short Text ClusteringCode0
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material DesignCode0
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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