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 1701–1725 of 3304 papers

TitleStatusHype
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
Model Order Reduction based on Runge-Kutta Neural Network—0
A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction—0
Measuring and modeling the motor system with machine learning—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack—0
Meta-learning of Pooling Layers for Character RecognitionCode0
On the Whitney near extension problem, BMO, alignment of data, best approximation in algebraic geometry, manifold learning and their beautiful connections: A modern treatmentCode0
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation—0
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks—0
Grassmann Graph Embedding—0
Empirical comparison between autoencoders and traditional dimensionality reduction methods—0
Simplicial RegularizationCode0
Low-Rank Isomap Algorithm—0
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features—0
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples—0
SCALE SPACE FLOW WITH AUTOREGRESSIVE PRIORS—0
Lazy FSCA for Unsupervised Variable SelectionCode0
FedPower: Privacy-Preserving Distributed Eigenspace Estimation—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