Sparse Principal Component Analysis (PCA)

Study Notes

Sparse Principal Component Analysis (Sparse PCA) is a variant of traditional Principal Component Analysis (PCA) that introduces sparsity constraints on the principal components.
Sparse PCA provides a more interpretable set of principal components, performs feature selection and dimensionality reduction simultaneously, and is more efficient than regular PCA in handling high-dimensional data.
Factor Analysis (FA) is a statistical technique used for dimensionality reduction and exploring the underlying structure in a dataset, assuming that observed variables are influenced by latent factors.

Two-steps clustering is a clustering algorithm used to group data points into clusters based on their similarities, particularly useful for dealing with large datasets or data sets containing both categorical and continuous variables.
Bi-clustering, also known as block clustering or co-clustering, is a data mining technique that allows simultaneous clustering of both rows and columns of a matrix, particularly useful for identifying groups of data items that exhibit consistent patterns across both rows and columns.
Latent clustering, also known as Latent Class Cluster Analysis (LCCA), is a powerful technique that can uncover hidden structures in data by identifying latent subgroups or classes.
K-means clustering is a popular and powerful unsupervised machine learning algorithm that can be used to group data points into distinct clusters based on their similarities.

Stochastic approach is a type of time series analysis that involves modeling the future behavior of a time series as uncertain and can be modeled using probability distributions.
Time series decomposition is a technique used to break down a time series into its underlying component, typically trend, seasonality, and residual components.
Linear trend model is a deterministic approach used to identify and model trends in time series, assuming that the time series follows a straight line trend over time.
Exponential growth or decay models are powerful tools for analyzing and forecasting a wide range of real-world processes that exhibit proportional rates of change over time.

ARIMA (Autoregression Integrated Moving Average) is a stochastic approach used to model time series data, combining autoregression and moving average.
GARCH model (Generalized Autoregression Conditional Heteroskedasticity) is used to capture and predict changing volition in time series data.
VAR (Vector Auto Regression) is a multivariate time series model used to capture the linear interdependencies among multiple time series.
VECM (Vector Error Correction Model) is a multivariate time series model used to analyze and forecast the long-term and short-term dynamics of co-integrated variables.