Titlebook: Elements of Dimensionality Reduction and Manifold Learning; Benyamin Ghojogh,Mark Crowley,Ali Ghodsi Textbook 2023 The Editor(s) (if appli

倒轉(zhuǎn)

用樹皮

Background on KernelsIn functional analysis—a field of mathematics—there are various spaces of either data points or functions. For example, the Euclidean space is a subset of the Hilbert space, while the Hilbert space itself is a subset of the Banach space. The Hilbert space is a space of functions and its dimensionality is often considered to be high.

adipose-tissue

Fisher Discriminant AnalysisFisher Discriminant Analysis (FDA) attempts to find a subspace that separates the classes as much as possible, while the data also become as spread as possible.

毛細血管

GROSS

Locally Linear EmbeddingLocally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction method that can be used for manifold embedding and feature extraction.

echnic

Laplacian-Based Dimensionality ReductionSpectral dimensionality reduction methods deal with the graph and geometry of data and usually reduce to an eigenvalue or generalized eigenvalue problem (see Chap. .).

GNAW

有罪

Valves

高興一回

Probabilistic Metric LearningIt was mentioned in Chap. . that metric learning can be divided into three types of learning—spectral, probabilistic and deep metric learning.