SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1276–1300 of 3304 papers

TitleStatusHype
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods—0
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data—0
An Experimental Study of Dimension Reduction Methods on Machine Learning Algorithms with Applications to Psychometrics—0
An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification—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
Comparison of feature extraction and dimensionality reduction methods for single channel extracellular spike sorting—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
Comparison of Methods in Skin Pigment Decomposition—0
Feature-Preserving Rate-Distortion Optimization in Image Coding for Machines—0
A Novel Approach for Intrinsic Dimension Estimation—0
Feature Selection: A perspective on inter-attribute cooperation—0
Feature Selection Based on Orthogonal Constraints and Polygon Area—0
First-order bifurcation detection for dynamic complex networks—0
Feature Selection for Network Intrusion Detection—0
Feature Selection for Short Text Classification using Wavelet Packet Transform—0
Feature selection in functional data classification with recursive maxima hunting—0
Feature selection or extraction decision process for clustering using PCA and FRSD—0
Feature Selection Using Reinforcement Learning—0
Flashlight Search Medial Axis: A Pixel-Free Pore-Network Extraction Algorithm—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