We are excited to welcome Prof. Alejandro Rodriguez Garcia from the Abdus Salam International Centre for Theoretical Physics (ICTP) to Enabla! In his lecture, Alex continues the topic started by Marcello and explores the use of unsupervised machine learning techniques in many-body quantum systems, highlighting how dimensionality reduction can illuminate structure within complex data. Particular emphasis is placed on the Principal Component Analysis (PCA) as a key method to maximize variance while reducing dimensionality. This lecture sets the stage for future topics such as clustering and manifold learning.

🎥 Join us for this #OpenAccess lecture and take advantage of Enabla's unique features to ask questions directly to Prof. Rodriguez Garcia and engage in discussions with the community: https://enabla.com/pub/1112/about

Don't miss this opportunity to enhance your knowledge in the intersection of data mining and quantum physics!

#MachineLearning #UnsupervisedLearning #DimensionalityReduction #QuantumSystems #PCA #DataMining #OpenScience

Biologists, stop putting UMAP plots in your papers

#UMAP is a powerful tool for exploratory data analysis, but without a clear understanding of how it works, it can easily lead to confusion and misinterpretation.

https://simplystatistics.org/posts/2024-12-23-biologists-stop-including-umap-plots-in-your-papers/

#clustering #dimensionalityreduction #dataviz

We just completed a new course on #DimensionalityReduction in #Neuroscience, and the full teaching material 🐍💻 is now freely available (CC BY 4.0 license):

🌍 https://www.fabriziomusacchio.com/blog/2024-10-24-dimensionality_reduction_in_neuroscience/

The course is designed to provide an introductory overview of the application of dimensionality reduction techniques for neuroscientists and data scientists alike, focusing on how to handle the increasingly high-dimensional datasets generated by modern neuroscience research.

#PythonTutorial #CompNeuro

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

“Regardless of how we do dimensionality reduction, if the assumptions and biases underlying a method are not understood then it can be possible to see things in the data that aren’t there. “

#PCA #DimensionalityReduction #statistics

https://doi.org/10.1073/pnas.2319169120

Why the simplest explanation isn’t always the best: #DimensionalityReduction such as #PCA can see structures that do not exist and miss structures that exist. The simplest explanation isn’t always the best.

✍️ Dyer & Kording ( @kordinglab) (2023)
🌍 https://www.pnas.org/doi/10.1073/pnas.2319169120

#DataAnalysis #CompNeuro

...
Addendae (cont'd)

Manifold hypothesis
https://en.wikipedia.org/wiki/Manifold_hypothesis

Many high-dimensional data sets (requiring many variables) in the real world actually lie along low-dimensional latent manifolds in that high-dimensional space (described by a smaller number of variables).

This principle may underpin the effectiveness of ML algorithms in describing high-dimensional data sets by considering a few common features.

#ManifoldHypothesis #manifolds #MachineLearning #DimensionalityReduction

🌌🔬 BEP39: the Dimensionality Reduction-Based Networks proposal (https://docs.google.com/document/d/1GTWsj0MFQedXjOaNk6H0or6IDVFyMAysrJ9I4Zmpz2E/edit?usp=sharing)! Capture high-dimensional brain data complexity and explore their lower-dimensional representation with BIDS. #BrainDataAnalysis #DimensionalityReduction

Ahh I've been so excited for this paper to come out for ages!! No affiliation, just think it's super cool:

"Collection Space Navigator" for exploring projections of visual art collections

Honestly, when I first saw this, it wasn't the art applications that intrigued me so much as the value it offers for understanding 'slices' through high-dimensional space.

Demo: https://collection-space-navigator.github.io/CSN/

Website: https://collection-space-navigator.github.io/

#machinelearning #dimensionalityreduction #arts #datavisualization

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

`It is considered a non-linear approach as the mapping cannot be represented as a linear combination of the original variables as possible in techniques such as principal component analysis, which also makes it more difficult to use for classification applications`

https://en.wikipedia.org/wiki/Sammon_mapping

#machineLearning #clustering #classification #featureExtraction #featureEngineering #featureSelection #featureRanking #dimensionalityReduction #nonlinear

A spectral method for assessing and combining multiple data visualizations https://www.nature.com/articles/s41467-023-36492-2.epdf Code: https://github.com/rongstat/meta-visualization #dataviz #datascience #python #matplotlib #dimensionalityreduction

#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

Alright, that's all for today's #quiz. As "usual" (OK, this is new here, but usual for Twitter I guess), answers and discussion in 2-3 days; in the meantime, please ask questions, or post comments below!

#dimensionalityreduction

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