Howtosignalprocessing
- What is ICA and PCA?
- Is ICA the same as PCA?
- How will you differentiate between PCA and ICA technique?
- What is PCA for machine learning?
What is ICA and PCA?
Independent Component Analysis (ICA)
Principal Component Analysis (PCA) ICA optimizes higher-order statistics such as kurtosis. PCA optimizes the covariance matrix of the data which represents second-order statistics. ICA finds independent components. PCA finds uncorrelated components.
Is ICA the same as PCA?
PCA vs ICA
Although the two approaches may seem related, they perform different tasks. Specifically, PCA is often used to compress information i.e. dimensionality reduction. While ICA aims to separate information by transforming the input space into a maximally independent basis.
How will you differentiate between PCA and ICA technique?
While the goal in PCA is to find an orthogonal linear transformation that maximizes the variance of the variables, the goal of ICA is to find the linear transformation, which the basis vectors are statistically independent and non-Gaussian.
What is PCA for machine learning?
The Principal Component Analysis is a popular unsupervised learning technique for reducing the dimensionality of data. It increases interpretability yet, at the same time, it minimizes information loss. It helps to find the most significant features in a dataset and makes the data easy for plotting in 2D and 3D.
