SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2551–2575 of 3304 papers

TitleStatusHype
Interpretable and Compositional Relation Learning by Joint Training with an AutoencoderCode0
Quantum classification of the MNIST dataset with Slow Feature Analysis—0
Spectral feature scaling method for supervised dimensionality reduction—0
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly—0
Distribution-based Label Space Transformation for Multi-label Learning—0
Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering—0
Transcription Factor-DNA Binding Via Machine Learning Ensembles—0
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining—0
Branching embedding: A heuristic dimensionality reduction algorithm based on hierarchical clusteringCode0
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs—0
Compressed Dictionary Learning—0
Secure Face Matching Using Fully Homomorphic EncryptionCode0
Bringing Order to Chaos: A Non-Sequential Approach for Browsing Large Sets of Found Audio Data—0
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis—0
Auto-Detection of Safety Issues in Baby Products—0
From Principal Subspaces to Principal Components with Linear AutoencodersCode0
Learning Manifolds from Non-stationary Streaming Data—0
Mathematical Analysis on Out-of-Sample Extensions—0
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification—0
varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets—0
Deep Embedding Kernel—0
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featuresCode0
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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