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 2526–2550 of 3304 papers

TitleStatusHype
Using pseudo-senses for improving the extraction of synonyms from word embeddings—0
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level GenerationCode0
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA—0
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral ImageryCode0
Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images—0
Parallel Transport Unfolding: A Connection-based Manifold Learning Approach—0
Overlapping Sliced Inverse Regression for Dimension Reduction—0
Virtual Codec Supervised Re-Sampling Network for Image Compression—0
Generalizing Correspondence Analysis for Applications in Machine Learning—0
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative FactorsCode0
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
CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes—0
TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection—0
Learning Restricted Boltzmann Machines via Influence Maximization—0
On the Estimation of Entropy in the FastICA AlgorithmCode0
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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