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A Multi-graph Spectral Framework for Mining Multi-source Anomalies,used in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today’s information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.

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Graph Embedding for Speaker Recognition,compassing multiple applications. At the core is the problem of speaker comparison—given two speech recordings (utterances), produce a score which measures speaker similarity. Using speaker comparison, other applications can be implemented—speaker clustering (grouping similar speakers in a corpus),

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Iris Bednarz-Braun,Ulrike Heß-Meiningused in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today’s information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.

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Improving Classifications Through Graph Embeddings,ng [5], medical diagnosis [15], demographic research [13], etc. Unsupervised classification using K-Means generally clusters data based on (1) distance-based attributes of the dataset [4, 16, 17, 23] or (2) combinatorial properties of a weighted graph representation of the dataset [8].

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Learning with ,,-Graphfor High Dimensional Data Analysis,ce learning, and semi-supervised learning. Data clustering often starts with a pairwise similarity graph and then translates into a graph partition problem [19], and thus the quality of the graph essentially determines the clustering quality.

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A Multi-graph Spectral Framework for Mining Multi-source Anomalies,used in a variety of domains, such as intrusion detection, fraud detection, and health monitoring. Today’s information explosion generates significant challenges for anomaly detection when there exist many large, distributed data repositories consisting of a variety of data sources and formats.