Free Information Center
t-SNE (t-distributed Stochastic Neighbor Embedding) is a machine learning algorithm used for dimensionality reduction and data visualization, particularly effective for high-dimensional datasets. It maps complex data into a lower-dimensional space while preserving local similarities.
Unsupervised learning is a machine learning paradigm that analyzes data without pre-labeled responses to identify patterns and structures.
Locally Linear Embedding (LLE) is a nonlinear dimensionality reduction technique used for data visualization and analysis in machine learning.
An autoencoder is a type of artificial neural network used to learn efficient data encodings in an unsupervised manner. It consists of an encoder to compress the input and a decoder to reconstruct the original data from the compressed representation.
The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces, often causing challenges in machine learning, data analysis, and numerical computation. These issues include exponential growth in volume, sparsity of data, and difficulties in distance measurement, which complicate tasks like classification and clustering.
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.
Laplacian eigenmaps is a dimensionality reduction technique used in machine learning and data analysis, particularly for nonlinear data structures.
Principal component analysis (PCA) is a statistical technique used to reduce the dimensionality of data by transforming it into a new set of variables called principal components. These components capture the maximum variance within the data, enabling easier visualization, interpretation, and noise reduction.
Linear Discriminant Analysis (LDA) is a statistical method for classifying data by finding a linear combination of features that best separates two or more classes.
Dimensionality reduction is a process in data analysis and machine learning that transforms data from a high-dimensional space into a lower-dimensional space while preserving essential properties. It facilitates visualization, reduces storage requirements, and helps improve the performance of algorithms by eliminating redundant or irrelevant features.