Title: Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Tabular Data
Venue: NeurIPS 2024
Accepted: no
Keywords: Outlier Detection

One-class classification

GAN

Active Learning

Tabular data
Abstract: Outlier detection in high-dimensional tabular data is an important task in data mining, essential for many downstream tasks and applications. Existing unsupervised outlier detection algorithms face one or more problems, including inlier assumption (IA), curse of dimensionality (CD), and multiple views (MV). To address these issues, we introduce Generative Subspace Adversarial Active Learning (GSAAL), a novel approach that uses a Generative Adversarial Network with multiple adversaries. These adversaries learn the marginal class probability functions over different data subspaces, while a single generator in the full space models the entire distribution of the inlier class. GSAAL is specifically designed to address the MV limitation while also handling the IA and CD, being the only method to do so. We provide a mathematical formulation of MV, convergence guarantees for the discriminators, and scalability results for GSAAL. Our extensive experiments demonstrate the effectiveness and scalability of GSAAL, highlighting its superior performance compared to other popular OD methods, especially in MV scenarios.


Peer reviews:
comments: SUMMARY:
This paper proposed GSAAL to simultaneously address three changeling problems in outlier detection: inlier assumption (IA), curse of dimensionality (CD), and multiple views (MV).

STRENGTHS:
The paper has a good flow.
The paper proposed the first outlier detection method that explicitly addresses IA, CD, and MV simultaneously. 
The paper has strong theoretical and empirical evidence to show the advancement of the proposed method. 
The experimental design is solid and the numerous visual examples help to facilitate understanding.
The paper has good reproducibility with open codes.

WEAKNESSES:
some (but few) places to improve.

QUESTIONS:
1. There are a lot of abbreviations in this article. Before using them, they should be first defined.
2. In line 96, “Classical Methods” lacks recently published work, such as “Mean-shift outlier detection and filtering”.
3. In line 159, it reads “p_x(x) = p_{ux}(ux) for almost all x” (Equation 1). Please classify whether x here refers only to normal samples or also to outlier samples. 
4. Why only K-NN-based baselines were shown in Fig. 3? How about other baselines? The font size of the text in Fig. 3 was too small.
5. Please explain the meaning of “FA” in Table 5.
6. The Y-axis in Figure 4 lacks a name.

LIMITATIONS:
The authors have analyzed the limitations sufficiently.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 4
DATE: 2024-07-11

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents this generalization of GAAL Generative Subspace Adversarial Active Learning (GSAAL) for outlier detection to address the limitation of the previous work such as multi-view and the curse of dimensionality, where the theoretical convergence, the scalability of the algorithm are discussed. Experiments on real dataset and synthetic tabular dataset are carried out to establish the validity of the approaches.\n\nSTRENGTHS:\nThe manuscript presents a method called generative subspace adversarial active learning for outlier detection in multiple views. The proposed method called GSAAL provides the proof of convergence, the computation complexity and aims to address the curse of dimensionality. The outlier detection in high dimensional space indeed is an important and challenging solution. Thus, the proposed method can be a good solution to address this difficult problem.\n\nThe manuscript has compared the performance of GSAAL with other outlier detection approaches with detailed visual illustration and AUC. The experiments show advantages of the proposed solution over other competing methods. The experiments seems to be detailed.\n\nWEAKNESSES:\nThe novelty of the work appears to be small. Theoretically, the derivation of theorem 1 is very similar to GAN derivation. \n\nIn this case, the paper needs to compare their solution both theoretically and experimentally with the related work for outlier detection using GAN such as [1] https://arxiv.org/pdf/1906.11632 such as AnoGAN, BioGAN and EGBAD. \n[2] https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-022-00943-7\nIf we compare the main equation (2) in the manuscript with the formulation in reference [1] with conditional GAN and BioGAN, it seems the main difference are the proposed method used multiple detectors and accumulated the performance, which should not be considered a large distinction.\n\nDue to the lack of the comparison with generative adversarial network based approaches such as AnoGAN and EGBAD, the potential improvement of the purposed method against the state-of-the-art approaches is not clear. The novelty of the paper does not stand on the safe ground. The theoretical derivation is also similar to GAN derivation.\n\nQUESTIONS:\nLike mentioned above, in order to convince the readers, the paper should really clarify the different and improvement of their solution compared to GAN based solution and focus on explaining and justifying whether or why the improvement against GAN (if there are) is significant.\n\nLIMITATIONS:\nLimited innovation and lack of critical comparison with important reference are the main issues of the current manuscript.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe main contribution of this paper is to improve existing work on Generative Adversarial Active Learning (GAAL) by using multiple discriminators for multiple views to detect outliers in tabular data. The training mechanism is similar to existing works. The paper also introduces a theoretical analysis on Multiple Views (MV). As claimed by the authors, GAAL addressed the problems of Inlier Assumption (IA) and Curse of Dimensionality (CD), but missed Multiple Views (MV), which is the main focus of this paper. The experimental results compare the proposed method to GAAL and some other classical methods such as OCSVM and KNN, ....\n\nSTRENGTHS:\nThe paper introduces an interesting view about MV and proposes a new method to address this MV problem together with theoretical analysis.\n\nWEAKNESSES:\n* The empirical results are not strong (or at least unclear in the way the authors presented in the main paper); most of the experiments are on synthetic datasets.\n* The results on the real dataset do not seem to show significant improvements compared to existing work (or at least it is hard to observe this when reading the paper). Perhaps the authors could improve the writing and highlight the results better. It is unclear to me why the experiments on the real dataset were put in the Appendix, as it is an important result.\n* The paper claims at the beginning that it not only improves the MV problem but also the IA and CD problems, but this is hard to see with the current writing of the paper. Could the authors highlight the experiments in the paper to prove that claim?\n\nQUESTIONS:\n1. How do you define the number of discriminators and the number of realizations u?\n2. In Fig. 3, why is GAAL missing? The paper claims it is faster in terms of time complexity. Which results show this?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-12"}