Adaptive multiple graph regularized semi-supervised extreme learning machine
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DOI: 10.1007/s00500-018-3109-x
Abstract: Semi-supervised extreme learning machine (SSELM) was proposed as an effective algorithm for machine learning and pattern recognition. However, the performance of SSELM heavily depends on whether the underlying geometrical structure of the data can be… read more here.
Keywords: machine; semi supervised; extreme learning; learning machine ... See more keywords
Multiple graph semi-supervised clustering with automatic calculation of graph associations
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DOI: 10.1016/j.neucom.2020.12.002
Abstract: Abstract Multiple graph clustering is an important tool in data integration and data mining for graph-based data. The prediction and classification accuracy can be significantly improved by integrating information from multiple sources and data sets.… read more here.
Keywords: graph semi; semi supervised; supervised clustering; association ... See more keywords
Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications
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DOI: 10.1109/tcsii.2018.2821241
Abstract: Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field.… read more here.
Keywords: sensing multiple; compressed sensing; rakeness based; based compressed ... See more keywords
Estimating Network Processes via Blind Identification of Multiple Graph Filters
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DOI: 10.1109/tsp.2020.2993780
Abstract: This paper studies the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel identification problem to graphs. More precisely, we model network processes as… read more here.
Keywords: network processes; multiple graph; identification; graph ... See more keywords