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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2526–2550 of 3304 papers

TitleStatusHype
Diffeomorphic brain shape modelling using Gauss-Newton optimisation—0
BinGAN: Learning Compact Binary Descriptors with a Regularized GANCode0
Introducing user-prescribed constraints in Markov chains for nonlinear dimensionality reductionCode0
DG-GL: Differential geometry based geometric learning of molecular datasets—0
Smart Analytical Signature Verification For DSP Applications—0
Feature selection in functional data classification with recursive maxima hunting—0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
Neural-Kernelized Conditional Density Estimation—0
Efficient and Scalable Batch Bayesian Optimization Using K-Means—0
Similarity encoding for learning with dirty categorical variablesCode0
TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection—0
CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes—0
On the Estimation of Entropy in the FastICA AlgorithmCode0
Learning Restricted Boltzmann Machines via Influence Maximization—0
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
Transcription Factor-DNA Binding Via Machine Learning Ensembles—0
A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering—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
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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