2 min read

Where and When to use t-SNE?

(t-SNE) t-Distributed Stochastic Neighbor Embedding is a non-linear dimensionality reduction algorithm used for exploring high-dimensional data. It maps multi-dimensional data to two or more dimensions suitable for human observation.
Where and When to use t-SNE?

9.1 Data Scientist

Well for the data scientist the main problem while using t-SNE is the black box type nature of the algorithm. This impedes the process of providing inferences and insights based on the results. Also, another problem with the algorithm is that it doesn’t always provide a similar output on successive runs.

So then how could you use the algorithm? The best way to used the algorithm is to use it for exploratory data analysis. It will give you a very good sense of patterns hidden inside the data. It can also be used as an input parameter for other classification & clustering algorithms.

9.2 Machine Learning Hacker

Reduce the dataset to 2 or 3 dimensions and stack this with a non-linear stacker. Using a holdout set for stacking / blending. Then you can boost the t-SNE vectors using XGboost to get better results.

9.3 Data Science Enthusiasts

For data science enthusiasts who are beginning to work with data science, this algorithm presents the best opportunities in terms of research and performance enhancements. There have been a few research papers attempting to improve the time complexity of the algorithm by utilizing linear functions. But an optimal solution is still required. Research papers on implementing t-SNE for a variety of NLP problems and image processing applications is an unexplored territory and has enough scope.

Guide to t-SNE machine learning algorithm implemented in R & Python
Learn the t-SNE machine learning algorithm with implementation in R & Python. t-SNE is an advanced non-linear dimensionality reduction technique

#T-SNE #MachineLearning #Probyto #ProbytoAI

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