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 3176–3200 of 3304 papers

TitleStatusHype
Compressive Feature Learning—0
Robust Transfer Principal Component Analysis with Rank Constraints—0
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation—0
Auto-adaptative Laplacian Pyramids for High-dimensional Data Analysis—0
Toward a unified theory of sparse dimensionality reduction in Euclidean space—0
Visualizing the Effects of a Changing Distance on Data Using Continuous EmbeddingsCode0
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems—0
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods—0
Applying Graph-based Keyword Extraction to Document Retrieval—0
Reconstructing Big Semantic Similarity Networks—0
Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging—0
Updating Rare Term Vector Replacement—0
An Image-Based Fluid Surface Pattern Model—0
Latent Fisher Discriminant Analysis—0
Sparse and Functional Principal Components AnalysisCode0
Thermal Human face recognition based on Haar wavelet transform and series matching technique—0
Some Options for L1-Subspace Signal Processing—0
SKYNET: an efficient and robust neural network training tool for machine learning in astronomy—0
Towards Basque Oral Poetry Analysis: A Machine Learning Approach—0
Learning from the past, predicting the statistics for the future, learning an evolving systemCode0
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Learning Deep Representation Without Parameter Inference for Nonlinear Dimensionality Reduction—0
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
On b-bit min-wise hashing for large-scale regression and classification with sparse data—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