Autoencoders vs t-SNE for Dimensionality Reduction
While preserving spatial relationships between data points in both lower and higher dimensions
Autoencoders (AEs) and t-SNE are two main techniques for dimensionality reduction.
Autoencoders are a type of neural network architecture with many practical applications. Dimensionality reduction is one of them. Autoencoders can be used for dimensionality reduction in non-linear data.
t-SNE is also a non-linear dimensionality reduction technique that can be used for visualizing high-dimensional data in a lower-dimensional space to find important clusters or groups in the data.
Both t-SNE and autoencoders can handle non-linear data which is very common in real-world applications.
However, autoencoders need a very large amount of data and computational resources to train the algorithm as it is a neural network architecture. In contrast, t-SNE is a general machine-learning algorithm that only needs a very small amount of data to train the algorithm.
In particular, t-SNE can preserve the spatial relationship between data points in both higher and lower dimensions after reducing the dimensionality of the data. It means that the nearby data points of the same class in the original…
