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Which outlier detection method is known to work well on high-dimensional data?
Therefore, it will be an interesting and efficient work to identify outliers from appropriate subspaces. As a popular technique used in the analysis of high-dimensional data, the subspace learning is also extensively studied in the field of outlier detection.
What are the challenges to outlier detection in high-dimensional data?
High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform poorly on data set of small size with a large number of features.
Which detection can handle high-dimensional data?
Angiulli and Pizzuti [30] proposed a distance-based anomaly detection technique called “HilOut” to detect the top n outliers of large and high-dimensional data sets.
What is meant by outlier detection?
Outlier detection is the process of detecting and subsequently excluding outliers from a given set of data. An outlier may be defined as a piece of data or observation that deviates drastically from the given norm or average of the data set.
Can you use outlier detection in high dimensional data?
High-dimensional data can cause serious issues when we apply many of the existing outlier detection methods. In model-based methods high-dimensional data results in high degrees of freedom which makes the objects statistically indistinguishable.
How to find outliers in a dataset?
In a standard outlier detection problem given an unlabeled dataset one must determine which of the instances do not belong to the majority of the data. We implicitly assume that the majority of the data is generated through a normal process and outliers are the instances that are not generated through the same mechanism.
“Outliers are often hidden in the unusual local behavior of low-dimensional subspaces, and this deviant behavior is masked by full-dimensional analysis.” C. C. Aggarwal Standard and widely used distance-based methods consist of computing the Mahalanobis distance. This class of methods only uses distance space to flag outlier observations.
Which is an example of an outlier in laser spectroscopy?
In the realm of laser spectroscopy, outliers are usually characterized by values that deviate significantly from the values of other observations, abnormally high or low — for example, humidity change, spatial heterogeneity on the samples surface, drift of the instrument parameters, human error, etc.