Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a ``manifold* close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.
This paper proposes to use the robust subspace recovery layer (RSR) in the autoencoder model for unsupervised anomaly detection. This paper is well written overall. Presentation is clear and it is easy to follow. The proposed approach is a simple combination of existing approaches. Although its theoretical analysis with respect to the performance of anomaly detection is limited, experiments show that the proposed method is effective and superior to the existing anomaly detection methods. I have the following comments: - Parameter sensitivity should be examined. The proposed method has the number of parameters including lambda_1, lambda_2, and parameters in neural networks. Since parameter tuning is fundamentally difficult in the unsupervised setting, the sensitivity of the proposed method with respect to changes of such parameters should be examined. - Since the efficiency is also an important issue for anomaly detection methods, runtime comparison would be interesting. - It would be also interesting whether the proposed method is also effective for non-structured data, where a dataset is given as just a set of (real-valued) feature vectors, with its comparison to the standard anomaly detection methods such as LOF and iForest.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection
We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a ``manifold* close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposes to use the robust subspace recovery layer (RSR) in the autoencoder model for unsupervised anomaly detection. This paper is well written overall. Presentation is clear and it is easy to follow. The proposed approach is a simple combination of existing approaches. Although its theoretical analysis with respect to the performance of anomaly detection is limited, experiments show that the proposed method is effective and superior to the existing anomaly detection methods. I have the following comments: - Parameter sensitivity should be examined. The proposed method has the number of parameters including lambda_1, lambda_2, and parameters in neural networks. Since parameter tuning is fundamentally difficult in the unsupervised setting, the sensitivity of the proposed method with respect to changes of such parameters should be examined. - Since the efficiency is also an important issue for anomaly detection methods, runtime comparison would be interesting. - It would be also interesting whether the proposed method is also effective for non-structured data, where a dataset is given as just a set of (real-valued) feature vectors, with its comparison to the standard anomaly detection methods such as LOF and iForest.
assistant
This paper proposes to use the robust subspace recovery layer (RSR) in the autoencoder model for unsupervised anomaly detection. This paper is well written overall. Presentation is clear and it is easy to follow. The proposed approach is a simple combination of existing approaches. Although its theoretical analysis with respect to the performance of anomaly detection is limited, experiments show that the proposed method is effective and superior to the existing anomaly detection methods. I have the following comments: - Parameter sensitivity should be examined. The proposed method has the number of parameters including lambda_1, lambda_2, and parameters in neural networks. Since parameter tuning is fundamentally difficult in the unsupervised setting, the sensitivity of the proposed method with respect to changes of such parameters should be examined. - Since the efficiency is also an important issue for anomaly detection methods, runtime comparison would be interesting. - It would be also interesting whether the proposed method is also effective for non-structured data, where a dataset is given as just a set of (real-valued) feature vectors, with its comparison to the standard anomaly detection methods such as LOF and iForest.