Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis
This provides theoretical insights into algorithm performance for high-dimensional data analysis, but it is incremental as it builds on existing methods without new applications.
The paper analyzes three subspace estimation algorithms (Oja's method, GROUSE, PETRELS) for streaming, incomplete data, showing that their principal angles converge to deterministic processes described by ODEs as dimension increases, with a convergence rate of O(1/√n).
We present a high-dimensional analysis of three popular algorithms, namely, Oja's method, GROUSE and PETRELS, for subspace estimation from streaming and highly incomplete observations. We show that, with proper time scaling, the time-varying principal angles between the true subspace and its estimates given by the algorithms converge weakly to deterministic processes when the ambient dimension $n$ tends to infinity. Moreover, the limiting processes can be exactly characterized as the unique solutions of certain ordinary differential equations (ODEs). A finite sample bound is also given, showing that the rate of convergence towards such limits is $\mathcal{O}(1/\sqrt{n})$. In addition to providing asymptotically exact predictions of the dynamic performance of the algorithms, our high-dimensional analysis yields several insights, including an asymptotic equivalence between Oja's method and GROUSE, and a precise scaling relationship linking the amount of missing data to the signal-to-noise ratio. By analyzing the solutions of the limiting ODEs, we also establish phase transition phenomena associated with the steady-state performance of these techniques.