Discriminative feature extraction by a neural implementation of canonical correlation analysis
The canonical correlation analysis (CCA) aims at measuring linear relationships between
two sets of variables (views) that can be used for feature extraction in classification problems …
two sets of variables (views) that can be used for feature extraction in classification problems …
Ensemble canonical correlation analysis
Abstract Canonical Correlation Analysis (CCA) aims at identifying linear dependencies
between two different but related multivariate views of the same underlying semantics …
between two different but related multivariate views of the same underlying semantics …
[PDF][PDF] Nonlinear Feature Extraction using Multilayer Perceptron based Alternating Regression for Classification and Multiple-output Regression Problems.
O Tiryaki, CO Sakar - DATA, 2018 - scitepress.org
Canonical Correlation Analysis (CCA) is a data analysis technique used to extract correlated
features between two sets of variables. An important limitation of CCA is that it is a linear …
features between two sets of variables. An important limitation of CCA is that it is a linear …
Feature extraction based on discriminative alternating regression
Abstract Canonical Correlation Analysis (CCA) aims at measuring linear relationships
between two sets of variables (views). Recently, CCA has been used for feature extraction in …
between two sets of variables (views). Recently, CCA has been used for feature extraction in …
Multi-view feature extraction based on canonical correlation analysis
CO Şakar - 2014 - acikbilim.yok.gov.tr
Canonical Correlation Analysis (CCA) aims at identifying linear dependenciesbetween two
sets of variables. CCA has recently become popular in the field of machinelearning with the …
sets of variables. CCA has recently become popular in the field of machinelearning with the …