Question

What is the purpose of principal component analysis?

Answer 1

★ Principal component analysis aims at reducing a large set of variables to a small set that still contains most of the information in the large set. Thetechnique of principal component analysis enables us to create and use a reduced set of variables, which are called principal factors.

Answer 2

Answer:

PCA is a technique for analysis that entails iteratively identifying the linear combination of a group of variables with the greatest variation, eliminating it, and repeating the process.

Explanation:

• High-dimensional data may be made simpler through the use of principal component analysis (PCA), while still preserving trends and patterns.
• It accomplishes this by condensing the data into fewer dimensions that serve as feature summaries.
• High-dimensional data are frequently found in biology and result from the measurement of various characteristics, such as the expression of numerous genes, for each sample. This sort of data poses a number of difficulties, which PCA lessens
• PCA uses a small number of principal components (PCs) to geometrically project data onto lesser dimensions in an effort to discover the best way to summarise the data.
• When fascinating patterns enhance the variance of projections onto orthogonal components, PCA is an effective method for summarising the data.

Learn more about principal component analysis here:

Does Principle component analysis determine the direction of maximum variance of data for a given feature set?

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About PCA

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