#ReleaseTuesday — New version of https://thi.ng/tsne with ~15-20% better performance[1] due to avoiding repeated internal allocations and skipping gradient updates where unnecessary...
[1] Benchmarked with multiple datasets of ~750 items, each with 192 dimensions (now ~165ms @ MBA M1, 2020)...
#ThingUmbrella #TSNE #DataViz #Visualization #ML #Cluster #TypeScript #JavaScript
my #tsne in #futhark (compiled to Python) iteratively spreading 10dimensional-clustered data (easy mode) into 2dimensions
it's not as fast as I hoped. In part because I'm not good at futhark yet, and in part because the Python target may not be optimized, however it's _fun_.
Based on "Visualizing Data using t-SNE" (Laurens van der Maaten and Geoffrey Hinton).
(click or leave your pointer on the image, 1, 5, 10, 15, and 25 iterations)
2D embeddings for high-dimensional data, the battle rages on
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012403
Может ли простейшая нейросеть найти математическую закономерность в данных?
В этой небольшой статье мы научим нейросеть решать задачу умножения перестановок длины 5 (группа ) и визуализируем результаты обучения с помощью методов проекции t-SNE (с понижением размерности PCA) и алгоритма UMAP. Мы убедимся в том, что даже элементарная модель может "неосознанно" провести бинарную классификацию перестановок.
Как анализировать тысячи отзывов с ChatGPT? Частые ошибки и пример на реальных данных
В этой статье я расскажу про свой опыт решения рабочей задачи — анализ отзывов о компании от пользователей. Мы разберем возможные ошибки и посмотрим на пример кода и реальных данных. Гайд будет полезен всем, у кого нет большого опыта в анализе данных или работе с LLM через API.
https://habr.com/ru/articles/821287/
#llm #gpt #chatgpt #python #clustering #kmeans #tsne #visualization #summarization #data_analysis
“Biostatistician Rafael Irizarry… dislikes many of the t-SNE and UMAP plots he sees. They offer little of value to a paper, he says, and the output from these tools is analytically intractable.”
New features in the mapdata.py data exploration tool (https://pypi.org/project/mapdata/) include:
1. Rank abundance plots. This visually displays species richness, evenness, and dominance for one or more sites.
2. Toggling of correlation coefficients in the correlation matrix so that a clean heatmap can be displayed.
3. Exclusion of missing data from t-SNE and UMAP analyses either case-wise or variable-wise.
#DataAnalysis #Ecology #Statistics #Correlation #UMAP #tSNE #Python #FOSS #FLOSS
Seeing data as t-SNE and UMAP do https://www.nature.com/articles/s41592-024-02301-x #bioinformatics #genomics #visualization #tsne #umap
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 😊🙏
The difference between mapping an n-dimensional feature space to three or two dimensions.
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
I made a short video of the strange things UMAP and t-SNE can do to your data. The algorithms are shown mostly working as intended, yet with some surprising consequences.
"certain kinds of important high-dimensional geometric features are, unfortunately, mathematically impossible to represent in the 2D Euclidean plane, no matter what method is used, leading to distorted visualizations and missed geometric, and potentially biological, insights at both local and global scales."
Wang et al. What cannot be seen correctly in 2D visualizations of single-cell ’omics data? Cell Syst. 2023 Sep 20;14(9):723–731. DOI: https://doi.org/10.1016/j.cels.2023.07.002
Read about our embedding technique:
https://arxiv.org/abs/2205.10271
Stay tuned for the Collection Space Navigator release and publication.
* attributed to George Box
#CollectionSpaceNavigator #CSN #curation #museum #visualization #collections #ai #wikiart #digitalhumanities #arthistory #UMAP #tSNE #research #complexity #deeplearning #embeddings #compression #science #dataanalytics #dataanalysis #aesthetics #curating #computervision
3/3
RT @LabListon@twitter.com: We have an exciting #computational paper out in @CellRepMethods. Ever use a #tSNE or #UMAP in #scSeq, #flowcytometry or #masscytometry? It doesn't have to be just a pretty picture anymore - we've developed a statistical test to check for differences. 1/6
https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(22)00295-8 https://twitter.com/LabListon/status/1615673972850413569/photo/1
