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 1801–1825 of 3304 papers

TitleStatusHype
Instance Space Analysis for the Car Sequencing Problem—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
Difficulty in estimating visual information from randomly sampled images—0
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques—0
Clustering high dimensional meteorological scenarios: results and performance index—0
A t-SNE Based Classification Approach to Compositional Microbiome Data—0
Recovery of Linear Components: Reduced Complexity Autoencoder Designs—0
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation—0
Spatial noise-aware temperature retrieval from infrared sounder data—0
Generating semantic maps through multidimensional scaling: linguistic applications and theory—0
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorizationCode0
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service—0
Multitask machine learning of collective variables for enhanced sampling of rare events—0
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare—0
K-Deep Simplex: Deep Manifold Learning via Local DictionariesCode0
A journey in ESN and LSTM visualisations on a language taskCode0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor EmbeddingCode0
Consistent Representation Learning for High Dimensional Data Analysis—0
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction—0
Learning sparse codes from compressed representations with biologically plausible local wiring constraintsCode0
Learning Feature Sparse Principal SubspaceCode0
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform—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