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 1201–1225 of 3304 papers

TitleStatusHype
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—0
Finding Rule-Interpretable Non-Negative Data Representation—0
Augmentation Component Analysis: Modeling Similarity via the Augmentation OverlapsCode0
AVIDA: Alternating method for Visualizing and Integrating Data—0
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time SeriesCode1
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
Principal Component Analysis based frameworks for efficient missing data imputation algorithms—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Trainable Weight Averaging: A General Approach for Subspace TrainingCode1
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs—0
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNECode0
PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes—0
The Forecasting performance of the Factor model with Martingale Difference errors—0
Spatial Transcriptomics Dimensionality Reduction using Wavelet BasesCode0
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series ForecastingCode1
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with TreemapsCode1
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterpartsCode1
Dimension Reduction for Efficient Dense Retrieval via Conditional AutoencoderCode1
Precoder Design for Correlated Data Aggregation via Over-the-Air Computation in Sensor Networks—0
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
Revisiting Classical Multiclass Linear Discriminant Analysis with a Novel Prototype-based Interpretable Solution—0
LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval—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