Machine Learning Concept -5: Dimensionality Reduction technique t-SNE.
Dimensionality Reduction Technique: t-SNE
=> t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique that is often used in machine learning to visualize high-dimensional data.
=> t-SNE is particularly useful for exploring and interpreting datasets that have many variables or dimensions, such as images, speech data, and text data.
=> Technically, t-SNE works by first calculating the pairwise distances between all data points in the high-dimensional space. It then creates a probability distribution that assigns a higher probability to nearby points and a lower probability to distant points. Next, it creates a similar probability distribution in a lower-dimensional space, and tries to minimize the difference between the two probability distributions. In other words, it tries to find a lower-dimensional representation of the data that preserves the similarities between data points in the high-dimensional space.
=> One of the reasons why t-SNE is important in machine learning is that it can reveal the underlying structure of high-dimensional data. This is especially important when dealing with large and complex datasets, such as images or speech data, where it may be difficult to discern…
