#tsne — Public Fediverse posts

Evaluation of concentrations of 35 PCB congeners in blood of about 5,000 individuals in the U.S. in 2003-2004 identified variations in PCB fingerprints by age, with a distinct pattern in a subset of younger individuals.

UMAP, t-SNE, and PCA were used for dimensionality reduction and pattern analysis. UMAP proved best at clustering data points with similar fingerprints.

https://www.tandfonline.com/doi/full/10.1080/15275922.2026.2690366?af=R

(edit: added missing link)

#Science #PCBs #DataAnalysis #UMAP #tSNE #PCA #RStats

Evaluation of concentrations of 35 PCB congeners in blood of about 5,000 individuals in the U.S. in 2003-2004 identified variations in PCB fingerprints by age, with a distinct pattern in a subset of younger individuals.

UMAP, t-SNE, and PCA were used for dimensionality reduction and pattern analysis. UMAP proved best at clustering data points with similar fingerprints.

https://www.tandfonline.com/doi/full/10.1080/15275922.2026.2690366?af=R

(edit: added missing link)

#Science #PCBs #DataAnalysis #UMAP #tSNE #PCA #RStats

Evaluation of concentrations of 35 PCB congeners in blood of about 5,000 individuals in the U.S. in 2003-2004 identified variations in PCB fingerprints by age, with a distinct pattern in a subset of younger individuals.

UMAP, t-SNE, and PCA were used for dimensionality reduction and pattern analysis. UMAP proved best at clustering data points with similar fingerprints.

https://www.tandfonline.com/doi/full/10.1080/15275922.2026.2690366?af=R

(edit: added missing link)

#Science #PCBs #DataAnalysis #UMAP #tSNE #PCA #RStats

Evaluation of concentrations of 35 PCB congeners in blood of about 5,000 individuals in the U.S. in 2003-2004 identified variations in PCB fingerprints by age, with a distinct pattern in a subset of younger individuals.

UMAP, t-SNE, and PCA were used for dimensionality reduction and pattern analysis. UMAP proved best at clustering data points with similar fingerprints.

https://www.tandfonline.com/doi/full/10.1080/15275922.2026.2690366?af=R

(edit: added missing link)

#Science #PCBs #DataAnalysis #UMAP #tSNE #PCA #RStats

Evaluation of concentrations of 35 PCB congeners in blood of about 5,000 individuals in the U.S. in 2003-2004 identified variations in PCB fingerprints by age, with a distinct pattern in a subset of younger individuals.

UMAP, t-SNE, and PCA were used for dimensionality reduction and pattern analysis. UMAP proved best at clustering data points with similar fingerprints.

https://www.tandfonline.com/doi/full/10.1080/15275922.2026.2690366?af=R

(edit: added missing link)

#Science #PCBs #DataAnalysis #UMAP #tSNE #PCA #RStats

🧠 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

🧠 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

🧠 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

🧠 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

🧠 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

Experimenting with using t-SNE to plot @boldsystems.bsky.social DNA barcodes in "sequence space" (in this case, defined by k-mer vectors). This example is bold-view-bf2dfe9b0db3.herokuapp.com/record/GMESB... where the BIN BOLD:ACP0173 looks to comprise two distinct clusters.

#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

#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

#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

#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

#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

2D embeddings for high-dimensional data, the battle rages on

#umap #tsne #scrnaseq

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012403

2D embeddings for high-dimensional data, the battle rages on

#umap #tsne #scrnaseq

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012403

2D embeddings for high-dimensional data, the battle rages on

#umap #tsne #scrnaseq

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012403

2D embeddings for high-dimensional data, the battle rages on

#umap #tsne #scrnaseq

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.

https://habr.com/ru/articles/851214/

#перестановки #переобучение #нейросеть #tsne #визуализация

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.

https://habr.com/ru/articles/851214/

#перестановки #переобучение #нейросеть #tsne #визуализация

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.

https://habr.com/ru/articles/851214/

#перестановки #переобучение #нейросеть #tsne #визуализация

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

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

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.”

https://www.nature.com/articles/s41592-024-02301-x

#PCA #UMAP #tSNE

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

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

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

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

The difference between mapping an n-dimensional feature space to three or two dimensions.

Source: https://lvdmaaten.github.io/tsne/ #tsne

The difference between mapping an n-dimensional feature space to three or two dimensions.

Source: https://lvdmaaten.github.io/tsne/ #tsne

The difference between mapping an n-dimensional feature space to three or two dimensions.

Source: https://lvdmaaten.github.io/tsne/ #tsne

The difference between mapping an n-dimensional feature space to three or two dimensions.

Source: https://lvdmaaten.github.io/tsne/ #tsne

The difference between mapping an n-dimensional feature space to three or two dimensions.

Source: https://lvdmaaten.github.io/tsne/ #tsne

I celebrate this! 😀
#Rstats #tSNE #MoMA

I celebrate this! 😀
#Rstats #tSNE #MoMA

I celebrate this! 😀
#Rstats #tSNE #MoMA

I celebrate this! 😀
#Rstats #tSNE #MoMA

I celebrate this! 😀
#Rstats #tSNE #MoMA

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

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

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

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

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

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

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

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