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Principal Component Analysis (PCA)

PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Its purpose is to reduce the dimensionality of the dataset, enhancing interpretability while minimizing information loss.

Isomap (Isometric Mapping)

Isomap is a global dimensionality reduction method that computes a lower-dimensional embedding which maintains geodesic distances between all points. It's an extension of MDS and PCA, capable of preserving the intrinsic geometry of the data as opposed to only focusing on local similarities.

t-Distributed Stochastic Neighbor Embedding (t-SNE)

t-SNE is a non-linear dimensionality reduction technique that is well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. It converts similarities between data points to joint probabilities and tries to minimize the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data.

Multidimensional Scaling (MDS)

MDS is a means of visualizing the level of similarity of individual cases of a dataset. It attempts to model similarity or dissimilarity of data by representing them as distances in a geometric space. The principal purpose is to detect meaningful underlying dimensions that explain observed similarities or dissimilarities.

Autoencoders

Autoencoders are a type of artificial neural network used to learn efficient codings of unlabeled data. The network consists of an encoder that compresses the data and a decoder that reconstructs it. The purpose is to minimize the difference between the input and the reconstructed output, thereby reducing dimensionality.

Linear Discriminant Analysis (LDA)

LDA is used as a dimensionality reduction technique in the machine learning field. It reduces dimensions by separating classes in the best possible way. The goal is to project the features in higher dimension space onto a lower-dimensional space.

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