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Isolation Forest is an algorithm for data anomaly detection using binary trees.It was developed by Fei Tony Liu in 2008. [1] It has a linear time complexity and a low memory use, which works well for high-volume data.
Anomaly detection is crucial in the petroleum industry for monitoring critical machinery. [20] Martí et al. used a novel segmentation algorithm to analyze sensor data for real-time anomaly detection. [20] This approach helps promptly identify and address any irregularities in sensor readings, ensuring the reliability and safety of petroleum ...
In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.
In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such ...
Therefore, it also can be interpreted as an outlier detection method. [1] It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed. The algorithm was first published by Fischler and Bolles at SRI International in 1981 ...
SPMF includes an implementation of the DBSCAN algorithm with k-d tree support for Euclidean distance only. Weka contains (as an optional package in latest versions) a basic implementation of DBSCAN that runs in quadratic time and linear memory. linfa includes an implementation of the DBSCAN for the rust programming language.
The model transitions from a time-invariant to a time-varying framework, which impacts both computation and efficiency. [2] [7] Mamba employs a hardware-aware algorithm that exploits GPUs, by using kernel fusion, parallel scan, and recomputation. [2]
The standard method for training RNN by gradient descent is the "backpropagation through time" (BPTT) algorithm, which is a special case of the general algorithm of backpropagation. A more computationally expensive online variant is called "Real-Time Recurrent Learning" or RTRL, [ 78 ] [ 79 ] which is an instance of automatic differentiation in ...