Introduction to Principal Component Analysis (PCA) for Beginners

In this webinar, graduate student Edwin Caballero offers an introduction to one of the most used algorithms for analyzing data. Principal component analysis is an algorithm that reduces the dimensionality of your data. Used a lot for analysis that involved more than 3 variables (multivariate) and recently essential for machine learning. ---------------------------------------------------- CHAPTERS 00:00 Intro 04:13 Applications 05:31 A bit of the Math 11:09 Residuals and Principal Component 14:00 What does Scores and Loadings Show 17:10 How Does it Work? 19:23 Inputs & Outputs 20:26 PCA Inputs 31:20 PCA Outputs 39:43 Where Can You Use PCA? ---------------------------------------------------- Mas información del presentador 👉 https://www.uprm.edu/ccs-cicsa/dt_tea... Desea saber mas sobre el grupo? Visita nuestra pagina! 👉 https://www.uprm.edu/ccs-cicsa/ Observa nuestros webinars/eventos 👉 https://www.uprm.edu/ccs-cicsa/news/ Observa nuestros proyectos 👉 https://www.uprm.edu/ccs-cicsa/our-work/ Observa nuestras facilidades 👉 https://www.uprm.edu/ccs-cicsa/facili... Principal Component Analysis (PCA) is a statistical method that is used to reduce the dimensionality of data. PCA is a type of linear transformation that projects the data onto a lower-dimensional space, such that the maximum amount of variance is preserved. This can be useful for visualizing the data, for removing noise and outliers, and for reducing the computational complexity of data analysis. PCA is commonly used in a variety of fields, including machine learning, computer vision, and chemometrics. In chemometrics, PCA is often used to analyze spectral data, such as infrared or Raman spectra. PCA can help researchers to identify the most important features in the data, to remove noise and outliers, and to visualize the data in a more meaningful way. It can also be used to reduce the dimensionality of the data, which can improve the performance of other data analysis methods, such as clustering or classification. Overall, PCA is a powerful and versatile tool for data analysis, and can be useful for researchers in a wide range of fields. It can help to simplify complex data sets, to identify important features, and to improve the accuracy and interpretability of data analysis. ---------------------------------------------------- #PCA #PrincipalComponentAnalysis #Chemometrics #Quimiometria #AnalisisDeComponentesPrincipales #Quimica #algoritmo #analisis #CCS-CISAC