Semi-paired Probabilistic Canonical Correlation Analysis
نویسندگان
چکیده
CCA is a powerful tool for analyzing paired multi-view data. However, when facing semi-paired multi-view data which widely exist in real-world problems, CCA usually performs poorly due to its requirement of data pairing between different views in nature. To cope with this problem, we propose a semi-paired variant of CCA named SemiPCCA based on the probabilistic model for CCA. Experiments with artificially generated samples demonstrate the effectiveness of the proposed method.
منابع مشابه
SemiCCA: Ef cient semi-supervised learning of canonical correlations
Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named semiCCAthat allows us to incorporate additional unpaired samples for mitigating over ttng. The p...
متن کاملA unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data
semi-supervised multi-view data Xiaohong Chen, Songcan Chen, Hui Xue , Xudong Zhou 1 Department of Mathematics, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, China 2 Department of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, China 3 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093...
متن کاملSparsification of Probabilistic Canonical Correlation Analysis
We have recently developed several ways of performing Canonical Correlation Analysis [1, 5, 7, 4] with probabilistic methods rather than the standard statistical tools. However, the computational demands of training such methods scales with the square of the number of samples, making these methods uncompetitive with e.g. artificial neural network methods [3, 2]. In this paper, we examine a rece...
متن کاملSemi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis
Kernel canonical correlation analysis (KCCA) is a dimensionality reduction technique for paired data. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying semantics of the data rather than noise. However, meaningful directions are not only those that have high correlation to another modality, but also those that capture the ma...
متن کاملMatrix-Variate Probabilistic Model for Canonical Correlation Analysis
Motivated by the fact that in computer vision data samples are matrices, in this paper, we propose a matrix-variate probabilistic model for canonical correlation analysis (CCA). Unlike probabilistic CCA which converts the image samples into the vectors, our method uses the original image matrices for data representation. We show that the maximum likelihood parameter estimation of the model lead...
متن کامل