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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 3126–3150 of 3304 papers

TitleStatusHype
Factors of Transferability for a Generic ConvNet Representation—0
Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction—0
Caffe: Convolutional Architecture for Fast Feature EmbeddingCode0
Dimensionality reduction for time series data—0
Unsupervised Deep Haar Scattering on Graphs—0
Linear Dimensionality Reduction: Survey, Insights, and GeneralizationsCode0
The constitution of visual perceptual units in the functional architecture of V1—0
Automated Disease Normalization with Low Rank Approximations—0
Merging SVMs with Linear Discriminant Analysis: A Combined Model—0
Transfer Joint Matching for Unsupervised Domain Adaptation—0
A Compact and Discriminative Face Track Descriptor—0
Grassmann Averages for Scalable Robust PCA—0
Hierarchical Feature Hashing for Fast Dimensionality Reduction—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning—0
Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors—0
The role of dimensionality reduction in linear classification—0
Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification—0
Comparing Similarity Measures for Distributional Thesauri—0
The AV-LASYN Database : A synchronous corpus of audio and 3D facial marker data for audio-visual laughter synthesis—0
How to Use less Features and Reach Better Performance in Author Gender Identification—0
Randomized Sketches of Convex Programs with Sharp Guarantees—0
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization—0
Subspace clustering of dimensionality-reduced data—0
Tight bounds for learning a mixture of two gaussians—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