Wavelet analysis/ dimension reduction

1. What is wavelet transform in dimensionality reduction?
2. What is dimension reduction analysis?
3. What are 3 ways of reducing dimensionality?
4. Which is the better technique for reducing dimensions?

What is wavelet transform in dimensionality reduction?

Wavelet Transforms − The discrete wavelet transform (DWT) is a linear signal processing technique that, when applied to a data vector X, transforms it to a numerically different vector, X', of wavelet coefficients. The two vectors are of a similar length.

What is dimension reduction analysis?

Dimensionality reduction is a machine learning (ML) or statistical technique of reducing the amount of random variables in a problem by obtaining a set of principal variables.

What are 3 ways of reducing dimensionality?

Principal Component Analysis (PCA), Factor Analysis (FA), Linear Discriminant Analysis (LDA) and Truncated Singular Value Decomposition (SVD) are examples of linear dimensionality reduction methods.

Which is the better technique for reducing dimensions?

Principal Component Analysis (PCA) is one of the most popular methods of dimensionality reduction as it is used for both data analysis and predictive modeling.