SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 3304 papers

TitleStatusHype
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features—0
Detection and Evaluation of Clusters within Sequential Data—0
Detection and Identification Accuracy of PCA-Accelerated Real-Time Processing of Hyperspectral Imagery—0
Classification of Cervical Cancer Dataset—0
Detection and tracking of gas plumes in LWIR hyperspectral video sequence data—0
Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images—0
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis—0
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies—0
DG-GL: Differential geometry based geometric learning of molecular datasets—0
Diagnosing ADHD from fMRI Scans Using Hidden Markov Models—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Diagnosis of Patients with Viral, Bacterial, and Non-Pneumonia Based on Chest X-Ray Images Using Convolutional Neural Networks—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Dictionary Learning under Symmetries via Group Representations—0
DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization—0
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Diffeomorphic Dimensionality Reduction—0
Attention or memory? Neurointerpretable agents in space and time—0
Differentially private sliced inverse regression in the federated paradigm—0
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm—0
Differential Privacy for Clustering Under Continual Observation—0
Classic machine learning methods—0
Difficulty in estimating visual information from randomly sampled images—0
An Impossibility Theorem for Node Embedding—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