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Large scale canonical correlation analysis with iterative least squares
Yichao Lu,Dean P. Foster +1 more
TLDR
L-CCA as mentioned in this paper is an iterative algorithm which can compute Canonical Correlation Analysis (CCA) fast on huge sparse datasets, and it is shown to outperform other fast CCA approximation schemes on two real datasets.Abstract:
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems. However, computing CCA for huge datasets can be very slow since it involves implementing QR decomposition or singular value decomposition of huge matrices. In this paper we introduce L-CCA, a iterative algorithm which can compute CCA fast on huge sparse datasets. Theory on both the asymptotic convergence and finite time accuracy of L-CCA are established. The experiments also show that L-CCA outperform other fast CCA approximation schemes on two real datasets.read more
Citations
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References
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