Review — t-SNE: Visualizing Data using t-SNE (Data Visualization)



Original Source Here

Review — t-SNE: Visualizing Data using t-SNE (Data Visualization)

Visualizing High-Dimensional Data in Low-Dimensional Space

t-SNE on MNIST Data set (From Google TechTalk by the First Author, Laurens van der Maaten, https://www.youtube.com/watch?v=RJVL80Gg3lA)

In this story, Visualizing Data using t-SNE, t-SNE, by Tilburg University, and University of Toronto, is briefly reviewed. It is a very famous paper by Prof. Hinton. In this paper:

  • t-SNE is proposed, compared to SNE, it is much easier to optimize.
  • t-SNE reduces the crowding problem, compared to SNE.
  • t-SNE has been used in various fields for data visualization.

This is a paper in 2008 JMLR with over 17000 citations. (Sik-Ho Tsang @ Medium) It was also presented in 2013 Google TechTalk by author.

AI/ML

Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot



via WordPress https://ramseyelbasheer.wordpress.com/2021/01/31/review%e2%80%8a-%e2%80%8at-sne-visualizing-data-using-t-sne-data-visualization/

Popular posts from this blog

Fully Explained DBScan Clustering Algorithm with Python

Image
Original Source Here Fully Explained DBScan Clustering Algorithm with Python Unsupervised learning in machine learning on density-based clusters Clustering In unsupervised Learning. A photo by Author In this article, we will discuss the machine learning clustering-based algorithm that is the DBScan cluster. The approach in this cluster algorithm is density-based than another distance-based approach. The other cluster which is distance-based looks for closeness in data points but also misclassifies if the point belongs to another class. So, density-based clustering is suited in this kind of scenario. The cluster algorithms come in unsupervised learning in which we don’t rely on target variables to make clusters. In a cluster, the main concern is on maximum dense connected points. The density-based clustering can be useful in arbitrary shapes even with noise. The working function of the density-based cluster is shown below: We select a random data point. Then it will ...
Read more

The 2021 machine learning, AI, and data landscape

Image
https://venturebeat.com/wp-content/uploads/2020/03/AI_winter-e1591205079457.jpg?w=1200&strip=all Original Source Here Just when you thought it couldn’t grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings , unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index ), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very large ( or in the case of Databricks, gigantic ) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index ). But...
Read more

Hierarchical clustering explained

Image
Original Source Here Interpretation of dendrogram Each level of dendrogram has a subtle meaning to the relationship between its data members. In a regular relationship chart, one may interpret that at the top lies grandparents or the first generation, the next level corresponds to parents or second generation and the final level belongs to children or third generation. Likewise, in every branching procedure of dendrogram, all the data points having the membership at each level belongs to a certain class. However, to infer this class entity, one has to go through a few individual samples of each level within the formulated cluster and find out what feature is common in the resulting cluster. Also, these inferred classes need not be similar at the sister branches. For example, in level 2, the cats have been clustered on big ears and calm behavior but dogs have been clustered on a similar attribute of size. Inferred classes of data in dendrogram by the user (Image by Author) C...
Read more