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 2551–2575 of 3304 papers

TitleStatusHype
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
Bringing Order to Chaos: A Non-Sequential Approach for Browsing Large Sets of Found Audio Data—0
Secure Face Matching Using Fully Homomorphic EncryptionCode0
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
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
Mathematical Analysis on Out-of-Sample Extensions—0
Deep Embedding Kernel—0
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier featuresCode0
Online Multi-Label Classification: A Label Compression Method—0
Robust Subspace Clustering with Compressed Data—0
Canonical Correlation Analysis of Datasets with a Common Source Graph—0
Reservoir computing approaches for representation and classification of multivariate time seriesCode0
Sparse Reduced Rank Regression With Nonconvex Regularization—0
Stock Price Prediction using Principle Components—0
Low-Rank Boolean Matrix Approximation by Integer Programming—0
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge—0
Construction of neural networks for realization of localized deep learning—0
Concise Fuzzy Planar Embedding of Graphs: a Dimensionality Reduction Approach—0
On Nonlinear Dimensionality Reduction, Linear Smoothing and Autoencoding—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