Dimensionality Reduction
726 papers with code • 0 benchmarks • 10 datasets
Dimensionality reduction is the task of reducing the dimensionality of a dataset.
( Image credit: openTSNE )
Benchmarks
These leaderboards are used to track progress in Dimensionality Reduction
Libraries
Use these libraries to find Dimensionality Reduction models and implementationsDatasets
CN-CELEB
Oxford105k
Oxford-Affine
AtariARI
GoodSounds
SoF
Deep Fakes Dataset
ALGAD
Latest papers
S+t-SNE - Bringing dimensionality reduction to data streams
We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams.
Assessing the similarity of real matrices with arbitrary shape
We conclude that SAS is a suitable measure for quantifying the shared structure of matrices with arbitrary shape.
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression
Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint.
GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition
deepintostreams/gcn-devlstm
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Skeleton-based action recognition (SAR) in videos is an important but challenging task in computer vision.
Curvature Augmented Manifold Embedding and Learning
A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed.
Discover and Mitigate Multiple Biased Subgroups in Image Classifiers
zhangaipi/dim
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Discovering biased subgroups is the key to understanding models' failure modes and further improving models' robustness.
Light Curve Classification with DistClassiPy: a new distance-based classifier
We explore the use of different distance metrics to aid in the classification of objects.
SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different Languages
Benchmarking plays a pivotal role in assessing and enhancing the performance of compact deep learning models designed for execution on resource-constrained devices, such as microcontrollers.
DiffRed: Dimensionality Reduction guided by stable rank
We rigorously prove that DiffRed achieves a general upper bound of $O\left(\sqrt{\frac{1-p}{k_2}}\right)$ on Stress and $O\left(\frac{(1-p)}{\sqrt{k_2*\rho(A^{*})}}\right)$ on M1 where $p$ is the fraction of variance explained by the first $k_1$ principal components and $\rho(A^{*})$ is the stable rank of $A^{*}$.
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series
This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models.
