Calculation of Histogram Features
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This document provides a comprehensive explanation of methods for calculating histogram features, including variance, kurtosis, and entropy. Variance quantifies data dispersion by measuring the average squared deviation from the mean, which can be implemented using functions like var() in MATLAB or numpy.var() in Python. Kurtosis describes the shape of data distribution, indicating peak sharpness and tail heaviness compared to a normal distribution; its calculation typically involves fourth-moment statistics accessible via libraries such as scipy.stats.kurtosis(). Entropy measures data uncertainty or information content, often computed using probability-based formulas like -sum(p * log(p)) where p represents histogram bin probabilities. By analyzing these histogram features, we gain deeper insights into data characteristics and distribution patterns, enabling more robust statistical analysis and machine learning preprocessing.
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