NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:6715
Title:Exponentially convergent stochastic k-PCA without variance reduction

Reviewer 1

POST-REBUTTAL UPDATE: I am happy with the authors' response to my main question. I maintain that this is a top paper and it should absolutely be accepted. ----------------- This was the best of the 6 papers I reviewed. Intuitive results, great idea and execution on the main result. The writing is very clear the discussion on related work thorough and detailed, and the paper overall is a joy to read. I believe that it should be accepted. Some critiques I had about the limitations of the proposed method (warm start which needs to use an initialization of rank proportional to the ambient dimension) are already discussed honestly in the paper, which also includes an effort to propose a heuristic method to “bootstrap” from a high-rank initialization progressively reducing the rank in a logarithmic number of phases. My main remaining question is the following: Your main result is given with high probability, in expectation. Could you elaborate with some discussion on what that guarantee means, and what it does not in terms of the actual convergence of the algorithm? In my opinion that’s the only piece of discussion missing from an otherwise great paper. Originality: The paper is motivated by a nuance in the known information-theoretic lower bounds for online stochastic PCA. The bounds suggest that the best possible rate is O(1/n), where n is the number of examples, but it critically relies on an assumption on the rank of the data. The authors point out how, for low rank matrices, this lower bound becomes uninformative, leaving the rank-constrain problem wide-open for improvement. The authors go on to do exactly that by proposing an algorithm that achieves exponentially fast convergence. Even though I have done some work in the area, I admit that I was unaware of this nuance in the lower bound. I have not seen other work exploit it in a similar way. Quality: The quality of the paper is excellent. From organization, writing, literature review, motivation and presentation of the results it is very well done and polished. Clarity: Excellent. I will recommend this paper to people interested in getting in the area and wanting to learn. Significance: Given the originality of the contributions 1 and 2 above, I think this paper can have a significant impact on the research community interested in similar problems. Minor typo, Line 252: “shaper” -> “sharper”

Reviewer 2

Originality: The method is novel and non-obvious. The original Krasulina method is literally SGD on the squared reconstruction error. However the generalization presented appears to be distinct from this form of generalization, and is instead motivated to preserve self-regulating variance decay as the residual decays. The relationship to related work, particularly VR-PCA, is explored in depth and adequately cited. Quality: The body of the submission is technically sound, and I have no reason to doubt the validity of the proofs in the supplement; the structure is reasonable, though I have not checked the arguments in detail. The authors are careful and honest about addressing the strengths and weaknesses of the method, in particular proposing Algorithm 2 to overcome a weakness in the assumptions in Theorem 1, acknowledging the desirability of extending the theoretical results to effectively low-rank data, and providing a detailed comparison with VR-PCA in theory and practice. However, the significance of this comparison would be strengthened by considering runtime, not just epochs. Clarity: The writing is very clear and well-organized. The introductory overview of approaches to PCA provides the reader with helpful orientation. There are two natural questions for which I’d appreciate further clarification: Significance: The results are highly significant given the centrality of PCA in data science and the modern context of online algorithms on big or streaming data. The method improves upon existing theory and practice.

Reviewer 3

***AFTER REBUTTAL*** After reading the response from the authors and the other reviewers' comments, I increase my score from 7 to 8. ***END OF COMMENT*** SUMMARY: The paper proposes an online stochastic learning algorithm to solve the non-convex problem of PCA. The proposed approach employs Krasulina’s method, which is generalised from vector to matrix representation. The idea is to demonstrate that the generalised Krasulina’s method is a stochastic gradient descent with a self-regulated gradient that can be tailored to solve the PCA problem. In addition to presenting a new application for Krasulina’s method, convergence rates are derived for low-rank and full-rank data. The derivation of the exponential convergence rate for low-rank data, in theorem 1, highlights that the convergence rate is actually affected by the intrinsic dimension, or number of components, and not by the dimensionality of the data. ORIGINALITY: The proposed method combines and tailors an old technique for a new purpose. It advances the research on online learning solutions to the PCA problem in the sense that the state-of-the-art approach, that combines the classic Oja’s method with a variance reduction step, is not an online algorithm because it requires a full pass over the dataset. The previous work in this field is well-referenced and accurately presented. QUALITY: The theoretical presentation is complete and justified with proofs. The empirical evaluation is performed in terms of a simulation study and two real-world experiments. In the real-world data experiments, the proposed algorithm is evaluated against the variance reduction augmented Oja’s method. In the simulation study, theorem 1 is shown to hold in practice. In the real-world experiments, the proposed approach is shown to converge faster before the competing method reaches a full pass. CLARITY: The paper is clearly written and well-organised. The results can probably be reproduced with the help of the pseudocode. SIGNIFICANCE: For practitioners working with large datasets, the presented method can be useful to reduce the dimensionality of the data.