Contents
- 1 What is the main difference between feature selection and dimensionality reduction?
- 2 Which of the following dimensionality reduction techniques preserves distances between points?
- 3 Why do we need to do dimensionality reduction?
- 4 How is dimensionality reduction introduced in Karl Pearson?
- 5 How is dimensionality reduction related to parsimony?
What is the main difference between feature selection and dimensionality reduction?
While both methods are used for reducing the number of features in a dataset, there is an important difference. Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension.
Which of the following dimensionality reduction techniques preserves distances between points?
Multidimensional Scaling (MDS) MDA is another non-linear dimensionality reduction technique that tries to preserve the distances between instances while reducing the dimensionality of non-linear data. There are two types of MDS algorithm: Metric and Non-metric.
Which type of algorithm should you use for dimensionality reduction?
Linear Discriminant Analysis, or LDA, is a multi-class classification algorithm that can be used for dimensionality reduction.
Why do we need to do dimensionality reduction?
Some of the prominent reasons which compel us to go in for dimensionality reduction are: Based on our hypotheses about possible features that may impact our prediction of the target feature, we may set about collecting data.
How is dimensionality reduction introduced in Karl Pearson?
This method was introduced by Karl Pearson. It works on a condition that while the data in a higher dimensional space is mapped to data in a lower dimension space, the variance of the data in the lower dimensional space should be maximum. It involves the following steps: Construct the covariance matrix of the data.
Which is the Prime linear method for dimensionality reduction?
The prime linear method, called Principal Component Analysis, or PCA, is discussed below. This method was introduced by Karl Pearson. It works on a condition that while the data in a higher dimensional space is mapped to data in a lower dimension space, the variance of the data in the lower dimensional space should be maximum.
All the efforts to reduce dimensionality are geared towards the Principle of Parsimony, which advocates the simplest possible explanation or prediction of a phenomenon with the fewest possible variables and assumptions.