A scree plot is a graphical representation used in principal component analysis (PCA) to determine the number of significant components by displaying the eigenvalues in descending order. The 'elbow' point on the plot indicates where adding more components yields diminishing returns, helping to decide the optimal number of components to retain for data reduction.

Scree Plot

scree plot

Graphical representation is a visual method of presenting data or information, allowing for easier interpretation and analysis by highlighting patterns, trends, and relationships. It encompasses various forms such as charts, graphs, and diagrams, each suited to different types of data and analytical needs.

graphical representation

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms a dataset into a set of orthogonal components ordered by the amount of variance they capture. It is widely used for feature extraction, noise reduction, and data visualization, especially in high-dimensional datasets.

principal component analysis

significant components

Eigenvalues are scalars associated with a linear transformation that, when multiplied by their corresponding eigenvectors, result in a vector that is a scaled version of the original vector. They provide insight into the properties of matrices, such as stability, and are critical in fields like quantum mechanics, vibration analysis, and principal component analysis.

eigenvalues

descending order

Elbow Point

Diminishing Returns, also known as the Law of Diminishing Marginal Returns, refers to a point at which the level of benefits gained is less than the amount of money or energy invested. This principle is crucial in economics and production, indicating that adding more of one factor of production, while holding others constant, will eventually yield lower per-unit returns.

diminishing returns

optimal number of components

Data reduction is a process aimed at minimizing the amount of data that needs to be stored or processed, while preserving the essential information and structure. This is crucial for improving efficiency, reducing storage costs, and enhancing the performance of data processing systems, especially in big data environments.

Relevant Degrees

Your Lessons