Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly con- front the problem of dimensionality reduction: Þnding meaningful low-dimen- sional structur
This book describes established and advanced methods for reducing the dimensionality of numerical databases. Each descr iption starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementa
Multi-Label dimensionality Reduction Multi-label learning concerns supervised learning problems in which each instance may be associated with multiple labels simultaneously. A key difference between multi-label learning and traditional binary or mul
Existing manifold learning algorithms use Euclidean distance to measure the proximity of data points. However, in high-dimensional space, Minkowski metrics are no longer stable because the ratio of distance of nearest and farthest neighbors to a give
