What do you mean by Principal coordinate analysis?
Principal Coordinates Analysis (PCoA,) is a method to explore and to visualize similarities or dissimilarities of data. It starts with
What is the difference between Multi-Dimensional Scaling and Principal Component Analysis?
Principal Component AnalysisThe input to PCA is the original vectors in n-dimensional space.And the data are projected onto the directions in the data
What do you mean by Multi-Dimensional Scaling (MDS)?
Multidimensional scaling is a visual representation of distances or dissimilarities between sets of objects. “Objects” can be colors, faces, map coordinates.
What are the differences between PCA and LDA?
Both LDA and PCA are linear transformation techniques: LDA is a supervised whereas PCA is unsupervised and ignores class labels.We
What are the Pros and cons of the PCA?
Advantages of Principal Component Analysis 1. Removes Correlated Features:In a real-world scenario, this is very common that you get thousands of
What do you mean by Singular Value Decomposition?
The Singular-Value Decomposition, or SVD for short, is a matrix decomposition method for reducing a matrix to its constituent parts
Explain about Eigen values and Eigen Vectors?
Eigenvector —Every vector (list of numbers) has a direction when it is plotted on an XY chart. Eigenvectors are those vectors
What do you mean by Linear Discriminant Analysis?
Linear Discriminant Analysis is a supervised algorithm as it takes the class label into consideration. It is a way to
What do you mean by Principal Component Analysis?
Principal component analysis is a technique for feature extraction so, it combines our input variables in a specific way, then we can
What is Dimensionality Reduction in Machine Learning?
Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal