PROJECTION PURSUIT DENSITY ESTIMATION

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Density Modeling of Images using a Generalized Normalization Transformation (2015, methodology): “Speciﬁcally, variants of ICA-MG transformations have been applied in iterative cascades to learn densities (Friedman et al., 1984; Chen & Gopinath, 2000; Laparra et al., 2011).”
DENSITY ESTIMATION BY DUAL ASCENT OF THE LOG-LIKELIHOOD ∗ (2010, background): “218 A DUAL ASCENT OF THE LOG-LIKELIHOOD pursuit [5], which seeks optimal directions for functional fitting.”
Dimension Reduction: A Guided Tour (2010, methodology): “Apart from handling high dimensional data, projection pursuit methods can be robust to noisy or irrelevant features [57], and have been applied to regression [35], where the regression is expressed as a sum of “ridge functions” (functions of the one-dimensional projections) and at each iteration the”
Cross-Validation and the Estimation of Conditional Probability Densities (2004, methodology): “There is a large literature on dimension reduction for density estimation, including work of Friedman, Stuetzle, and Schroeder (1984) and Jones and Sibson (1987).”
Continuous latent variable models for dimensionality reduction and sequential data reconstruction (2001, methodology): “Projection pursuit density approximation (Friedman et al., 1984) uses a multiplicative composition so that the estimate can be nonnegative and integrate to 1:”