Split-Merge Evolutionary Clustering for Multi-View Streaming Data

Devagiri, Vishnu Manasa; Boeva, Veselka; Tsiporkova, Elena

doi:10.1016/j.procs.2020.08.048

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…Therefore, these vectors were directly applied to dual-view data for selecting shared potential subspaces. Further development of these vectors was realized in multiview regression [39] . Some existing works consider data stream classification based on multi-view learning.…”

Section: Multi-view Learning For Data Stream Classificationmentioning

confidence: 99%

Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning

Wang,

Shi,

Zhang

et al. 2024

Big Data Min. Anal.

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With the enhancement of data collection capabilities, massive streaming data have been accumulated in numerous application scenarios. Specifically, the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors. Existing incremental learning methods are often single-task single-view, which cannot learn shared representations between relevant tasks and views. An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges, utilizing the idea of multi-task multi-view learning. Specifically, the attention mechanism is first used to align different sensor data of different views. In addition, MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning. Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.

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Section: Multi-view Learning For Data Stream Classificationmentioning

confidence: 99%

Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning

Wang,

Shi,

Zhang

et al. 2024

Big Data Min. Anal.

View full text Add to dashboard Cite

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“…Another challenge addressed by DIASeN is the development of advanced AI algorithms for continual, shared, and evolving learning that enable learning from multiple data sources by distributed training and continual updating of the model. This can be achieved by developing unsupervised and semisupervised methods to automate knowledge extraction and learning in data stream scenarios [4], [5]. The main problem investigated is how the newly arrived information can be taken into account in the learning phase and can be used for continuous adaptation of the learned model [6].…”

Section: Research Objectivesmentioning

confidence: 99%

“…1) dynamic unsupervised and semi-supervised learning models that are robust to the appearance of drifting context and additionally enable to learn from multiple data sources by distributed training, and continual updating and evolving of the model [4], [6], [7], [8]; 2) development of dynamic techniques for automatic annotation (labeling) of the data; 3) usage of transfer learning techniques enabling reuse of knowledge from training in earlier tasks to subsequent tasks. The other research ambition of DAISeN is the design of distributed/composable data mining models.…”

Section: Research Objectivesmentioning

confidence: 99%

“…Such models will allow one to run the lighter but less accurate models at the edge for the sake of latency, and the computation-intensive but higher-accuracy models in the cloud. The focus is on developing data mining and ML techniques to maintain local models embedded in the edge devices and further integrate low-level edge devices' observations into a global model [4], [5], [9]. Computed at a higher level, the latest can produce reliable decisions based on the available input data.…”

Section: Research Objectivesmentioning

confidence: 99%

“…According to [58], different phases of an evolutionary clustering algorithm can be categorized into matching, accommodating new data, and model refinement. Dynamic clustering is also a form of online/incremental unsupervised learning [4], [6], [7], [9], [60]. However, it considers the incremental fashion of building the clustering model and self-adaptation of the built model.…”

Section: B Current State Of the Art And Challengesmentioning

confidence: 99%

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Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN)

Boeva¹,

Casalicchio²,

Abghari³

et al. 2022

Annals of Computer Science and Information Systems

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This position paper describes the aims and preliminary results of the Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN) 1 project. The project ambition is to address today's edge AI challenges by developing advanced AI techniques that model knowledge from the sensor network and the environment to support the deployment of sustainable AI applications. We present one of the use cases being considered in DAISeN and review the state-of-the-art in three research domains related to the use case presented and directly falling into the project scope. We additionally outline the main challenges identified in each domain. The developed Global Navigation Satellite Systems (GNSS) activation model addressing the use case challenges is also briefly introduced. The future research studies planned for the remaining period of the project are finally outlined. I. INTRODUCTIONT HE NUMBER of solutions that provide Artificial Intelligence (AI) and Machine Learning (ML) based systems has been growing recently. These solutions facilitate the creation of new smart products and services in many different fields. In addition, sensor networks are undergoing great expansion and development and the integration of AI and sensor networks benefits many areas such as Industry 4.0, healthcare, mobility, logistics, and many other Internetof-Things (IoT) applications. However, this has also put new challenges in front of researchers and practitioners. New realtime AI and ML algorithms are needed along with different strategies to embed these algorithms in sensor boards and network nodes such as fog/edge nodes. For example, edgebased AI requires robust and adaptive models that take into account the temporal component of a data flow and allow for vertical and horizontal scaling of the decision-making process. These models must employ efficient learning algorithms that are capable of dealing with information varying over time and coping with large scale missing and inaccurate values. In addition, the decision-making models should be composable so that they can be distributed on the edge devices in order to This work is part of the Sony RAP 2020 Project "Distributed and Adaptive Edge-based AI Models for Sensor Networks"1 Daisen is a volcanic mountain located in Tottori Prefecture, Sanin Region of Japan. ensure a trade-off between the decision accuracy, latency, and consumed energy per decision.The IoT is an emerging key technology for future industries and the everyday lives of people, e.g., it has been playing an increasingly important role in healthcare, agriculture, home services, industrial processes, and transportation. Wireless Sensor Network (WSN) is an enabling technology for IoT [1], and, by definition, is the bridge between the physical world and the intelligence residing on the Internet. The integration of AI and sensor networks (by means of the IoT) are now realities that are changing our lives. Sensor networks are widely used to collect environmental parameters, e.g., in homes, buildings, and vehicles, where they a...

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