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 2801–2825 of 3304 papers

TitleStatusHype
Visual response properties of MSTd emerge from a sparse population code—0
Exemplar-Centered Supervised Shallow Parametric Data Embedding—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Maximally Correlated Principal Component Analysis—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
Predicting Pairwise Relations with Neural Similarity EncodersCode1
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
Computational Techniques in Multispectral Image Processing: Application to the Syriac Galen Palimpsest—0
Representation of big data by dimension reduction—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
Stochastic Multidimensional Scaling—0
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension—0
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
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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