t-SNE
Explore how t-SNE applies nonlinear manifold learning to reduce high-dimensional data for effective visualization. Understand its advantages over PCA, the process of creating t-SNE models, and how to interpret clustered data representations.
We'll cover the following...
What is t-SNE
Manifold learning is an approach to non-linear dimensionality reduction. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high.
We are not talking about the topic of manifold learning. The purpose of talking about manifold learning is that we would use this technique to do some visualization on our dataset. In the real world, many datasets are very high dimensional data. Such high dimensional data can’t be visualized in a 2D or 3D space. You may think that we can use the dimensionality reduction to process the data, such as PCA, to 2-dimension, and plot it. Manifold Learning can be ...