SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1801–1825 of 3304 papers

TitleStatusHype
Fast emulation of cosmological density fields based on dimensionality reduction and supervised machine-learning—0
Faster Discovery of Faster System Configurations with Spectral Learning—0
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training—0
Faster variational inducing input Gaussian process classification—0
Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming—0
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method—0
Fast Multi-Group Gaussian Process Factor Models—0
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis—0
Fast Robust PCA on Graphs—0
FAST-Splat: Fast, Ambiguity-Free Semantics Transfer in Gaussian Splatting—0
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods—0
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data—0
Feasibility of Principal Component Analysis in hand gesture recognition system—0
Feature-aware Label Space Dimension Reduction for Multi-label Classification—0
Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study—0
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction—0
Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses—0
Feature Hashing for Language and Dialect Identification—0
Feature Hashing for Large Scale Multitask Learning—0
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns—0
Feature-Preserving Rate-Distortion Optimization in Image Coding for Machines—0
Feature Selection: A perspective on inter-attribute cooperation—0
Feature Selection Based on Orthogonal Constraints and Polygon Area—0
Feature Selection for Network Intrusion Detection—0
Feature Selection for Short Text Classification using Wavelet Packet Transform—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