Different data compression techniques

[bkhanal-11]

What are key differences between Autoencoder, PCA and SVD?

[bkhanal-11]

Autoencoder, Principal Component Analysis (PCA), and Singular Value Decomposition (SVD) are all methods for reducing the dimensionality of data. However, there are several key differences between these methods.

Autoencoders are a type of neural network that can be used to learn a compressed representation of input data. Unlike PCA and SVD, autoencoders can learn non-linear relationships between the input data, which can make them more effective at capturing complex patterns and features in the data. Autoencoders are also capable of generating new data samples that are similar to the input data, which can be useful for tasks such as data augmentation and image generation.

PCA is a statistical technique that uses linear algebra to transform high-dimensional data into a lower-dimensional representation. PCA works by identifying the principal components of the data, which are the directions in the data that explain the most variance. These principal components can then be used to project the data onto a lower-dimensional subspace. PCA is a linear technique and can be limited in its ability to capture non-linear relationships between the input data.

SVD is a matrix factorization technique that can be used for dimensionality reduction. SVD works by decomposing a data matrix into three matrices, where the middle matrix represents the singular values of the data. The singular values can be used to select the most important components of the data, which can then be used for projection into a lower-dimensional subspace. SVD is a linear technique and can be limited in its ability to capture non-linear relationships between the input data.

[KhanalBijay]

There is another compression technique with vector quantization and kmeans. More info here.