Novelty Detection and Online Learning for Chunk Data Streams

Dynamic Submodular-Based Learning Strategy in Imbalanced Drifting Streams for Real-Time Safety Assessment in Nonstationary Environments

The design of real-time safety assessment (RTSA) approaches in nonstationary environments is meaningful to reduce the possibility of significant losses. However, several challenging problems are needed to be well considered. The performance of existing approaches will be negatively affected in the settings of imbalanced drifting streams. In this case, the model design with the incremental update should also be explored. Furthermore, the query strategy should also be well-designed. This article investigates a dynamic submodular-based learning strategy to address such issues. Specifically, an efficient incremental update procedure is designed with the structure of the broad learning system (BLS), which is beneficial to the detection of concept drift. Furthermore, a novel dynamic submodular-based annotation with an activation interval strategy is proposed to select valuable samples in imbalanced drifting streams. The lower bound of annotation value is also proven theoretically with a novel drift adaption mechanism. Numerous experiments are conducted with the realistic data of JiaoLong deep-sea manned submersible. The experimental results show that the proposed approach can achieve better assessment accuracy than typical existing approaches.

Theoretical Analysis of Null Foley-Sammon Transform and its Implications

Null Foley-Sammon Transform (NFST) has received increasing attention in the machine learning and pattern recognition literature. NFST finds a discriminative nullspace where all samples of the same class get mapped into a single point. It has a closed form solution and is free of parameters to tune. NFST has been leveraged in many areas including novelty detection, person or vehicle re-identification and achieved state-of-the-art results. Motivated from its attractive properties and its effectiveness in wide range of applications, in this paper we focus on the theoretical analysis of NFST. In previous literature, NFST was shown to exist in small sample size (SSS) case. We first prove that NFST can exist in non-SSS case also, under certain conditions. Thereby, we extend the domain of applicability of NFST to a more general case. Secondly, we perform analysis of the singular points of NFST, revealing important insights on their identities and existence. Thirdly, we show the theoretical relation between NFST of SSS data and NFST of the non-SSS data obtained by PCA. Fourthly, we show that this theoretical relation can be exploited to obtain an efficient algorithm for computing NFST on high dimensional SSS data. Finally, we perform extensive experiments to validate our theoretical analysis.

Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies

Recently, anomaly scores have been formulated using reconstruction loss of the adversarially learned generators and/or classification loss of discriminators. Unavailability of anomaly examples in the training data makes optimization of such networks challenging. Attributed to the adversarial training, performance of such models fluctuates drastically with each training step, making it difficult to halt the training at an optimal point. In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions. For this purpose, we propose a method that utilizes the current state as well as an old state of the same generator to create good and bad quality reconstruction examples. The discriminator is trained on these examples to detect the subtle distortions that are often present in the reconstructions of anomalous data. In addition, we propose an efficient generic criterion to stop the training of our model, ensuring elevated performance. Extensive experiments performed on six datasets across multiple domains including image and video based anomaly detection, medical diagnosis, and network security, have demonstrated excellent performance of our approach.

Online group streaming feature selection using entropy-based uncertainty measures for fuzzy neighborhood rough sets

Online group streaming feature selection, as an essential online processing method, can deal with dynamic feature selection tasks by considering the original group structure information of the features. Due to the fuzziness and uncertainty of the feature stream, some existing methods are unstable and yield low predictive accuracy. To address these issues, this paper presents a novel online group streaming feature selection method (FNE-OGSFS) using fuzzy neighborhood entropy-based uncertainty measures. First, a separability measure integrating the dependency degree with the coincidence degree is proposed and introduced into the fuzzy neighborhood rough sets model to define a new fuzzy neighborhood entropy. Second, inspired by both algebra and information views, some fuzzy neighborhood entropy-based uncertainty measures are investigated and some properties are derived. Furthermore, the optimal features in the group are selected to flow into the feature space according to the significance of features, and the features with interactions are left. Then, all selected features are re-evaluated by the Lasso model to discard the redundant features. Finally, an online group streaming feature selection algorithm is designed. Experimental results compared with eight representative methods on thirteen datasets show that FNE-OGSFS can achieve better comprehensive performance.