منابع مشابه
Bayesian Sparse Partial Least Squares
Partial least squares (PLS) is a class of methods that makes use of a set of latent or unobserved variables to model the relation between (typically) two sets of input and output variables, respectively. Several flavors, depending on how the latent variables or components are computed, have been developed over the last years. In this letter, we propose a Bayesian formulation of PLS along with s...
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Orthonormalized partial least squares (OPLS) is often used to find a low-rank mapping between inputs X and outputs Y by estimating loading matrices A and B. In this paper, we introduce sparse orthonormalized PLS as an extension of conventional PLS that finds sparse estimates of A through the use of the elastic net algorithm. We apply sparse OPLS to the reconstruction of presented images from BO...
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Partial least squares (PLS) is a well known dimension reduction method which has been recently adapted for high dimensional classification problems in genome biology. We develop sparse versions of the recently proposed two PLS-based classification methods using sparse partial least squares (SPLS). These sparse versions aim to achieve variable selection and dimension reduction simultaneously. We...
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BACKGROUND Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (SPLS), may provide insights into the brain's mechanisms by finding relationships between neuroimaging and clinical/demographic data. The identificat...
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Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table. When the goal is to find the shared information between two tables, the ap...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2013
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco_a_00524