peer reviewedWe propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of PCA, our work extends recent methods for non-linear dimensionality reduction to the case where multiple embeddings of the same underlying low dimensional coordinates are observed, each lying on a different high dimensional manifold
Abstract. We present a fast algorithm for approximate canonical correlation analysis (CCA). Given a ...
Partial canonical correlation analysis (partial CCA) is a statistical method that estimates a pair o...
This paper describes and demonstrates Canonical Correlation Analysis (CCA) with orthogonal rotation ...
We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or ...
International audienceWe propose a non-linear Canonical Correlation Analysis (CCA) method which work...
Canonical correlation analysis (CCA) is a natural generalization of PCA when the data contain two se...
We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn com-plex nonlinear transf...
The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correla...
Non-linear canonical correlation analysis is a method for canonical correlation analysis with optima...
Abstract- Canonical correlation analysis (CCA) is a major linear subspace approach to dimensionality...
Abstract. Canonical correlation analysis (CCA) is a classical multivariate method concerned with des...
Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen-Loeve ...
International audienceCanonical correlation analysis (CCA) is a well-known technique used to charact...
In this letter, we present a method of two-dimensional canonical correlation analysis (2D-CCA) where...
Dimension reduction is an important topic in data mining and machine learning. Especially dimension ...
Abstract. We present a fast algorithm for approximate canonical correlation analysis (CCA). Given a ...
Partial canonical correlation analysis (partial CCA) is a statistical method that estimates a pair o...
This paper describes and demonstrates Canonical Correlation Analysis (CCA) with orthogonal rotation ...
We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or ...
International audienceWe propose a non-linear Canonical Correlation Analysis (CCA) method which work...
Canonical correlation analysis (CCA) is a natural generalization of PCA when the data contain two se...
We introduce Deep Canonical Correlation Analysis (DCCA), a method to learn com-plex nonlinear transf...
The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correla...
Non-linear canonical correlation analysis is a method for canonical correlation analysis with optima...
Abstract- Canonical correlation analysis (CCA) is a major linear subspace approach to dimensionality...
Abstract. Canonical correlation analysis (CCA) is a classical multivariate method concerned with des...
Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen-Loeve ...
International audienceCanonical correlation analysis (CCA) is a well-known technique used to charact...
In this letter, we present a method of two-dimensional canonical correlation analysis (2D-CCA) where...
Dimension reduction is an important topic in data mining and machine learning. Especially dimension ...
Abstract. We present a fast algorithm for approximate canonical correlation analysis (CCA). Given a ...
Partial canonical correlation analysis (partial CCA) is a statistical method that estimates a pair o...
This paper describes and demonstrates Canonical Correlation Analysis (CCA) with orthogonal rotation ...