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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Spectral methods are based on decomposing moment tensors. If data are generated from latent variable models, their empirical moment tensors have a kind of (approximate) low-rank decomposition (approximate due to both noisy observations and finite sample estimation error in the empirical moments). These decompositions can be computed from the moment tensors, e.g. using a kind of power iteration method on related (symmetrized) tensors. The basic ideas of these spectral methods are fairly well established and many examples have been explored, especially in [6]. This paper applies spectral fitting methods to data modeled as being generated from an IBP, including two common emission models (a linear gaussian model and a sparse factor analysis model, described in Section 2). The main ingredients are a calculation of the appropriate moment tensors and corresponding symmetrized versions (Section 3), an application of the standard tensor power decomposition method (Section 4), and concentration proofs to offer recovery guarantees when data are generated from the model (Section 5).