Computer Vision News - March 2019

21 Focus on Computer Vision News Local Correlation Integral (LOCI) - LOCI is very effective at detecting both individual outliers and clusters of outliers. The method produces a LOCI plot for every data point, which summarizes the information about the data points in the area surrounding that point. It determines clusters, micro-clusters, the diameter of clusters and distances between clusters. And from these measurements determines the degree of anomaly of the data point. Feature Bagging - Feature bagging fuses a number of base classifiers to improve prediction accuracy, fusion can use simple methods like averaging or median, or more sophisticated ones. Local Outlier Factor is used as the default Outlier Detector, but any other outlier detection algorithm, such as kNN or ABOD, may be substituted. As stated, PyOD can be run on either Python 2 or 3, using package six. It’s based on NumPy, SciPy and SciKit-Learn, and uses Keras for advanced neural network methods such as autoencoders and SO_GAAL. To improve scalability, all algorithms were optimized with JIT and Numba, and the library supports parallel processing on multi-processor computers. The PyOD API, inspired by the SciKit-Learn API, is basically a replica of that well- known interface. In particular, it includes the following: 1) the fit function, which trains the model and collects the appropriate statistics. 2) decision_function, which ranks outliers for every new data-point, once the model is trained. 3) predict, which returns a binary label for each data point. 4) predict_prob, which delivers the result as a probability measure. 5) fit_predict, which corresponds to calling the predict function, after performing fit. The package is available as a Python open source toolbox and it can be easily extended to implement new methods. New models are very easy to train within this framework (using the unified and well-known API), and you can use polymorphism to easily inherit any function and implement it for your own needs. How to install the package - as simple as: Example: Testing 4 methods on random generated dataset Python open source toolbox for Outlier Detection pip install pyod