Large scale canonical correlation analysis with iterative least squares

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.

References in corpus (1)

• Two Step CCA: A new spectral method for estimating vector models of words

Cited by in corpus (14)

• Generalized Canonical Correlation Analysis: A Subspace Intersection Approach
• Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis
• Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data
• Large-Scale Approximate Kernel Canonical Correlation Analysis
• Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis
• The iCanClean Algorithm: How to Remove Artifacts using Reference Noise Recordings
• Nonlinear Multiview Analysis: Identifiability and Neural Network-assisted Implementation
• Stochastic Canonical Correlation Analysis
• Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations
• Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
• Correlational Neural Networks
• Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
• Gen-Oja: A Two-time-scale approach for Streaming CCA
• Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis