We present low-rank methods for event detection  . We assume that normal observation come from a low-rank subspace, prior to being corrupted by a uniformly distributed noise. Correspondingly, we aim at recovering a representation of the subspace , and perform event detection by running point-to-subspace distance query in \ell^\infty, for each incoming observation . In particular, we use a variant of matrix completion under interval uncertainty on a suitable flattening M \in R^{m \times n of the input data to obtain a low-rank model M \approx L \times R, L \in R^{m \times r, R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of each incoming x \in R^n to the space spanned by R . For the distance computation, we present a constant-time algorithm with a one-sided error bounded by a function of the number of coordinates employed  .
Monitoring of streamed data to detect abnormal behavior (variously known as event detection, anomaly detection, change detection, or outlier detection) underlies many applications of the Internet of Things. Here, we propose a novel framework for event detection in high-dimensional data across a variety of sources, with asynchronous sampling and missing data, to instantly predict abnormal behavior . We assume that normal observations come from a low-rank subspace, prior to being corrupted by a uniformly distributed noise. Correspondingly, we aim to recover a representation of the subspace and perform event detection by running point-to-subspace distance queries on this subspace for incoming data . In particular, we use a variant of low-rank factorisation, which considers interval uncertainty sets around "known entries", on a suitable flattening of the input data to obtain a low-rank model , R \in R^{r \times n}, r \ll m} . On-line, we compute the distance of incoming data to the low-rank "normal" subspace and update the subspace to keep it consistent with the seasonal changes present . For the distance computation, we present an algorithm with a one-sided error bounded by a function of the number of coordinates employed . In our experimental evaluation, we have tested the ability of the proposed algorithm to identify samples of abnormal behavior in induction-loop data from Dublin, Ireland .
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We present low-rank methods
Monitoring of streamed data to detect abnormal behavior (variously known as event detection, anomaly detection, change detection, or outlier detection) underlies many applications of the Internet of Things. Here, we propose a novel framework
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in high-dimensional data across a variety of sources, with asynchronous sampling and missing data, to instantly predict abnormal behavior
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observation
observations
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at recovering
to recover
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,
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query in \ell^\infty, for each incoming observation
queries on this subspace for incoming data
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matrix completion under interval uncertainty
low-rank factorisation, which considers interval uncertainty sets around "known entries",
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M \in R^{m \times n
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M \approx L \times R, L \in R^{m \times r
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each incoming x \in R^n to the space spanned by R
incoming data to the low-rank "normal" subspace and update the subspace to keep it consistent with the seasonal changes present
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a constant-time
an
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. In our experimental evaluation, we have tested the ability of the proposed algorithm to identify samples of abnormal behavior in induction-loop data from Dublin, Ireland