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 1126–1150 of 3304 papers

TitleStatusHype
Empowering Digital Agriculture: A Privacy-Preserving Framework for Data Sharing and Collaborative Research—0
Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraints—0
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching—0
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—0
Emulators for stellar profiles in binary population modeling—0
CASS: Cross Adversarial Source Separation via Autoencoder—0
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION—0
Enhanced CNN with Global Features for Fault Diagnosis of Complex Chemical Processes—0
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis—0
Efficient Optimization for Discriminative Latent Class Models—0
An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images—0
Enhanced Sampling with Machine Learning: A Review—0
Causal Deep Learning—0
A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information—0
Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images—0
Enhancing IoT Security Against DDoS Attacks through Federated Learning—0
Enhancing literature review with LLM and NLP methods. Algorithmic trading case—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Enhancing Robustness of Machine Learning Systems via Data Transformations—0
CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference—0
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation—0
Evaluating the Stability of Deep Learning Latent Feature Spaces—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