We consider the problem of finding a suitable common low dimensional subspace for accurately representing a given set of covariance matrices. With one covariance matrix, this is principal component analysis (PCA). For multiple covariance matrices, we term the problem Common Component Analysis (CCA).While CCA can be posed as a tensor decomposition problem, standard approaches to tensor decompositions have two critical issues: (i) tensor decomposition methods are iterative and rely on the initialization; (ii) for a given level of approximation error, it is difficult to choose a suitable low dimensionality. In this paper, we present a detailed analysis of CCA that yields an effective initialization and iterative algorithms for the problem. The proposed methodology has provable approximation guarantees w.r.t. the global maximum and also allows one to choose the dimensionality for a given level of approximation error. We also establish conditions under which the methodology will achieve the global maximum. We illustrate the effectiveness of the proposed method through extensive experiments on synthetic data as well as on two real stock market datasets, where major financial events can be visualized in low dimensions.