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 2176–2200 of 3304 papers

TitleStatusHype
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
Large Scale Behavioral Analytics via Topical Interaction—0
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI—0
Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction and Multisensory Unsupervised Cause-effect Learning—0
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning—0
Latent Diffusion Model for Generating Ensembles of Climate Simulations—0
Latent diffusion models for parameterization and data assimilation of facies-based geomodels—0
Latent Dynamic Networked System Identification with High-Dimensional Networked Data—0
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes—0
Controlling for sparsity in sparse factor analysis models: adaptive latent feature sharing for piecewise linear dimensionality reduction—0
Latent Fisher Discriminant Analysis—0
Latent Gaussian process with composite likelihoods and numerical quadrature—0
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck—0
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification—0
Latent Space Representation of Electricity Market Curves for Improved Prediction Efficiency—0
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections—0
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization—0
Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation—0
Learned SVD: solving inverse problems via hybrid autoencoding—0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
Learning a Discriminative Null Space for Person Re-identification—0
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas—0
Learning-Augmented Sketches for Hessians—0
Learning automata based SVM for intrusion detection—0
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations—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