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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 1976–2000 of 3304 papers

TitleStatusHype
Sign Bits Are All You Need for Black-Box AttacksCode1
BayesOpt Adversarial AttackCode1
The Information Bottleneck Problem and Its Applications in Machine Learning—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!Code1
Memory-efficient training with streaming dimensionality reduction—0
ivis Dimensionality Reduction Framework for Biomacromolecular SimulationsCode3
Spectral Learning on Matrices and Tensors—0
Multi-Objective Evolutionary approach for the Performance Improvement of Learners using Ensembling Feature selection and Discretization Technique on Medical data—0
Understanding Aesthetic Evaluation using Deep Learning—0
Latent regularization for feature selection using kernel methods in tumor classificationCode0
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer—0
Estimating a Brain Network Predictive of Stress and Genotype with Supervised AutoencodersCode0
TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural NetworksCode0
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach—0
Geomstats: A Python Package for Riemannian Geometry in Machine LearningCode2
Backprojection for Training Feedforward Neural Networks in the Input and Feature SpacesCode0
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese NetworksCode0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
Weighted Fisher Discriminant Analysis in the Input and Feature SpacesCode0
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders—0
DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approach—0
Company classification using machine learning—0
Flows for simultaneous manifold learning and density estimationCode1
A new set of cluster driven composite development indicators—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