LAUSR

Efficient locality-sensitive hashing over high-dimensional streaming data

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0 Published in 2020 at "Neural Computing and Applications"

DOI: 10.1007/s00521-020-05336-1

Abstract: Approximate nearest neighbor (ANN) search in high-dimensional spaces is fundamental in many applications. Locality-sensitive hashing (LSH) is a well-known methodology to solve the ANN problem. Existing LSH-based ANN solutions typically employ a large number of… read more here.

Keywords: streaming data; sensitive hashing; dimensional streaming; high dimensional ... See more keywords

Applying temporal dependence to detect changes in streaming data

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1 Published in 2018 at "Applied Intelligence"

DOI: 10.1007/s10489-018-1254-7

Abstract: Detection of changes in streaming data is an important mining task, with a wide range of real-life applications. Numerous algorithms have been proposed to efficiently detect changes in streaming data. However, the limitation of existing… read more here.

Keywords: streaming data; changes streaming; applying temporal; temporal dependence ... See more keywords

Gradient-Based Training of Gaussian Mixture Models for High-Dimensional Streaming Data

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0 Published in 2021 at "Neural Processing Letters"

DOI: 10.1007/s11063-021-10599-3

Abstract: We present an approach for efficiently training Gaussian Mixture Model (GMM) by Stochastic Gradient Descent (SGD) with non-stationary, high-dimensional streaming data. Our training scheme does not require data-driven parameter initialization (e.g., k-means) and can thus… read more here.

Keywords: high dimensional; streaming data; gaussian mixture; training gaussian ... See more keywords

A Multi-Tier Stacked Ensemble Algorithm to Reduce the Regret of Incremental Learning for Streaming Data

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1 Published in 2018 at "IEEE Access"

DOI: 10.1109/access.2018.2868114

Abstract: Incremental Learning (IL) is an exciting paradigm that deals with classification problems based on a streaming or sequential data. IL aims to achieve the same level of prediction accuracy on streaming data as that of… read more here.

Keywords: incremental learning; streaming data; algorithm; multi tier ... See more keywords

Rethinking Visual Analytics for Streaming Data Applications

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0 Published in 2017 at "IEEE Internet Computing"

DOI: 10.1109/mic.2017.2911428

Abstract: Visual analytics is entering a period of renewed growth due to a shift in focus from static to streaming data applications. In this article, the authors illustrate several challenges arising from this pivot and suggest… read more here.

Keywords: analytics streaming; visual analytics; streaming data; rethinking visual ... See more keywords

Online and Unsupervised Anomaly Detection for Streaming Data Using an Array of Sliding Windows and PDDs

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1 Published in 2021 at "IEEE Transactions on Cybernetics"

DOI: 10.1109/tcyb.2019.2935066

Abstract: In this article, we propose an online and unsupervised anomaly detection algorithm for streaming data using an array of sliding windows and the probability density-based descriptors (PDDs) (based on these windows). This algorithm mainly consists… read more here.

Keywords: streaming data; online unsupervised; array; unsupervised anomaly ... See more keywords

Visual Structural Assessment and Anomaly Detection for High-Velocity Data Streams

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0 Published in 2021 at "IEEE Transactions on Cybernetics"

DOI: 10.1109/tcyb.2020.2973137

Abstract: The widespread use of Internet-of-Things (IoT) technologies, smartphones, and social media services generates huge amounts of data streaming at high velocity. Automatic interpretation of these rapidly arriving data streams is required for the timely detection… read more here.

Keywords: streaming data; inc; data streams; high velocity ... See more keywords

Energy-Efficient Superconductor Bloom Filters for Streaming Data Inspection

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0 Published in 2020 at "IEEE Transactions on Dependable and Secure Computing"

DOI: 10.1109/tdsc.2018.2843318

Abstract: Bloom filters can be used in network intrusion detection systems to detect known attack signatures in packet payloads. In this paper we propose and analyze the potential application of superconductor flux quantum technology for streaming… read more here.

Keywords: streaming data; superconductor; bloom; bloom filters ... See more keywords

Autonomous Learning Multimodel Systems From Data Streams

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0 Published in 2018 at "IEEE Transactions on Fuzzy Systems"

DOI: 10.1109/tfuzz.2017.2769039

Abstract: In this paper, an approach to autonomous learning of a multimodel system from streaming data, named ALMMo, is proposed. The proposed approach is generic and can easily be applied also to probabilistic or other types… read more here.

Keywords: streaming data; multimodel systems; system; autonomous learning ... See more keywords

Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression

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1 Published in 2022 at "IEEE Transactions on Fuzzy Systems"

DOI: 10.1109/tfuzz.2020.3039681

Abstract: As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural… read more here.

Keywords: topology; fuzzy random; data regression; streaming data ... See more keywords

Cost-Effective Incremental Deep Model: Matching Model Capacity With the Least Sampling

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3 Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2021.3132622

Abstract: Most existing approaches often utilize the pre-fixed structure and large number of labeled data for training complex deep models, which are difficult to implement on incremental scenarios. As a matter of fact, real-world data is… read more here.

Keywords: streaming data; cost effective; capacity; deep model ... See more keywords