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 3251–3300 of 3304 papers

TitleStatusHype
A Class-Based Agreement Model for Generating Accurately Inflected Translations—0
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Analyse en Composante Principale pour l'extraction des i-vecteurs en v\'erification du locuteur (Principal Component Analysis for i-vector extraction in speaker verification.) [in French]—0
Modeling coherence in ESOL learner texts—0
Forecastable Component Analysis (ForeCA)—0
This also affects the context - Errors in extraction based summaries—0
A hierarchical approach with feature selection for emotion recognition from speech—0
Scikit-learn: Machine Learning in PythonCode0
Book Review: Graph-Based Natural Language Processing and Information Retrieval by Rada Mihalcea and Dragomir Radev—0
Demixed Principal Component Analysis—0
Variational Gaussian Process Dynamical Systems—0
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data—0
Dimensionality Reduction Using the Sparse Linear Model—0
Sparse Manifold Clustering and Embedding—0
How to Evaluate Dimensionality Reduction? - Improving the Co-ranking MatrixCode0
Randomized Dimensionality Reduction for k-means Clustering—0
Kernel Methods for the Approximation of Nonlinear Systems—0
Face Recognition using Curvelet Transform—0
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction—0
Vector Diffusion Maps and the Connection LaplacianCode0
Unsupervised Kernel Dimension Reduction—0
Inductive Regularized Learning of Kernel Functions—0
Penalized Principal Component Regression on Graphs for Analysis of Subnetworks—0
Efficient Optimization for Discriminative Latent Class Models—0
Optimal learning rates for Kernel Conjugate Gradient regression—0
Worst-Case Linear Discriminant Analysis—0
Random Projections for k-means Clustering—0
Generative Local Metric Learning for Nearest Neighbor Classification—0
Robust PCA via Outlier PursuitCode0
Human Face Recognition using Line Features—0
Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform for Human Face Recognition—0
Sparse Metric Learning via Smooth Optimization—0
Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming—0
Multi-Label Prediction via Sparse Infinite CCA—0
STDP enables spiking neurons to detect hidden causes of their inputs—0
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction—0
Probabilistic Relational PCA—0
Hilbert space embeddings and metrics on probability measures—0
Feature Hashing for Large Scale Multitask Learning—0
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification—0
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity—0
Localized Sliced Inverse Regression—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Bayesian Exponential Family PCA—0
Supervised Exponential Family Principal Component Analysis via Convex Optimization—0
Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method—0
Diffeomorphic Dimensionality Reduction—0
Visualizing Data using t-SNE—0
3D Face Recognition with Sparse Spherical Representations—0
People Tracking with the Laplacian Eigenmaps Latent Variable Model—0
Show:102550
← PrevPage 66 of 67Next →
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