#dimensionalityreduction — Public Fediverse posts

🧠 New comprehensive review on #LowDimensional #embeddings of #HighDimensional data. Discusses how #dimensionalityreduction helps visualizing, exploring, and #modeling #ComplexSystems. From #PCA to #tSNE, #UMAP & #NeuralNetworks: Excellent overview paper👌

🌍 https://arxiv.org/abs/2508.15929

#CompNeuro #MachineLearning #DataVisualization

These four #Python #tutorials introduce and discuss #PCA, #tsne, #factoranalysis, and #Autoencoder as powerful tools for #DimensionalityReduction:

🌍 https://www.fabriziomusacchio.com/blog/2023-06-16-pca_with_python/
🌍 https://www.fabriziomusacchio.com/blog/2023-06-12-tsne_vs_pca/
🌍 https://www.fabriziomusacchio.com/blog/2023-06-16-factoranalysis_with_python/
🌍 https://www.fabriziomusacchio.com/blog/2023-06-16-autoencoder_with_python/

Feel free to share, use and remix 😊🙏

#tutorial

By the way this is the original article that presents t-SNE. Published 11/2008
https://jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
T-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data in 2 or 3 dimensions.
#DataVisualization #tSNE #MachineLearning #DimensionalityReduction #DataScience #AI #DataAnalysis #DataAnalytics

Differences in visualizing high-dimensional data with #UMAP, #tSNE and #PCA using the Collection Space Navigator
https://collection-space-navigator.github.io/
#CollectionSpaceNavigator #DataVisualization #MultidimensionalData #DimensionalityReduction #MachineLearning #OpenSource #ResearchTool @schichmax @mcanet @andreskarjus @tillmannohm

from the standpoint of model selection, parsimony often boils down to dimensionality reduction

#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss

#arxivfeed :

"Get a new perspective on EEG: Convolutional neural network encoders for parametric t-SNE."
https://www.biorxiv.org/content/10.1101/2022.12.08.519691v1

#Neuroscience #Neuro #Brain #Neuroimaging #EEG #DimensionalityReduction #tSNE #DeepLearning #NeuralNetworks