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 1101–1150 of 3304 papers

TitleStatusHype
Efficient Sketching Algorithm for Sparse Binary Data—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis—0
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs—0
EigenGS Representation: From Eigenspace to Gaussian Image Space—0
ELA: Efficient Local Attention for Deep Convolutional Neural Networks—0
Elastic deep autoencoder for text embedding clustering by an improved graph regularization—0
Electrical Grid Anomaly Detection via Tensor Decomposition—0
Elementos da teoria de aprendizagem de máquina supervisionada—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Elliptical modeling and pattern analysis for perturbation models and classfication—0
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth—0
Embedding Compression for Efficient Re-Identification—0
Embedding Feature Selection for Large-scale Hierarchical Classification—0
Embedding Functional Data: Multidimensional Scaling and Manifold Learning—0
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature—0
EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—0
Empirical comparison between autoencoders and traditional dimensionality reduction methods—0
Empirical Evaluation of Kernel PCA Approximation Methods in Classification Tasks—0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data—0
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