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

dimensionality reduction technique

orthogonal components

Feature extraction is a process in data analysis where raw data is transformed into a set of features that can be effectively used for modeling. It aims to reduce the dimensionality of data while retaining the most informative parts, enhancing the performance of machine learning algorithms.

feature extraction

Noise reduction refers to the process of removing or minimizing unwanted sound or data from a signal to improve its quality and clarity. It is crucial in various fields, including audio engineering, telecommunications, and image processing, to enhance user experience and data interpretation.

noise reduction

Data visualization is the graphical representation of information and data, which leverages visual elements like charts, graphs, and maps to provide an accessible way to see and understand trends, outliers, and patterns in data. It is a crucial step in data analysis and decision-making, enabling stakeholders to grasp complex data insights quickly and effectively.

data visualization

high-dimensional datasets

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Principal Component Analysis (PCA)

Principal Component in Dimensionality Reduction

Subsequent Components in Principal Component Analysis

Singular Value Decomposition

Key Principles of Dimensionality Reduction and Principal Component Analysis

Dimensionality Reduction

Feature Discovery in AI Modeling

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