Unsupervised learning
Unsupervised learning is a machine learning paradigm that analyzes data without pre-labeled responses to identify patterns and structures.
data analysis
Locally linear embedding (LLE)
Locally Linear Embedding (LLE) is a nonlinear dimensionality reduction technique used for data visualization and analysis in machine learning.
Spectral clustering
Spectral clustering is a technique in machine learning that uses eigenvalues of a similarity matrix to reduce dimensionality before clustering data points.
Non-negative matrix factorization (NMF)
Non-negative matrix factorization (NMF) is a mathematical technique used in data analysis and machine learning to decompose non-negative matrices into a product of non-negative factors.
Fully parameterized quantile function (FQF)
The Fully Parameterized Quantile Function (FQF) is a statistical approach used to estimate quantiles across various distributions, enhancing flexibility in modeling.
Kernel method
The kernel method is a powerful technique used in machine learning and statistical analysis for pattern recognition and data transformation.
Causal graph
A causal graph is a visual representation of causal relationships among variables, often used in statistics and data analysis.
Logistic regression
Logistic regression is a statistical method for predicting binary classes. It models the probability of a certain class or event occurring.
Logit lens
The Logit lens is a statistical tool used in various fields to model and analyze categorical outcomes. It is particularly significant in fields like economics, healthcare, and social sciences.
Laplacian eigenmaps
Laplacian eigenmaps is a dimensionality reduction technique used in machine learning and data analysis, particularly for nonlinear data structures.