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 676–700 of 3304 papers

TitleStatusHype
Detection and Identification Accuracy of PCA-Accelerated Real-Time Processing of Hyperspectral Imagery—0
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation—0
Applying Dimensionality Reduction as Precursor to LSTM-CNN Models for Classifying Imagery and Motor Signals in ECoG-Based BCIsCode0
Thinking Outside the Box: Orthogonal Approach to Equalizing Protected Attributes—0
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches—0
Bounds on Representation-Induced Confounding Bias for Treatment Effect EstimationCode0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach—0
Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL—0
Finding Real-World Orbital Motion Laws from Data—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images—0
The optimal resolution level of a protein is an emergent property of its structure and dynamicsCode0
Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized methodCode0
High Dimensional Binary Choice Model with Unknown Heteroskedasticity or Instrumental Variables—0
Cricket Player Profiling: Unraveling Strengths and Weaknesses Using Text Commentary Data—0
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent—0
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraftCode2
Differentiable VQ-VAE's for Robust White Matter Streamline EncodingsCode0
Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications—0
Perfecting Liquid-State Theories with Machine Intelligence—0
Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative ValuesCode0
Computing Approximate _p Sensitivities—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