## 2 Related Work  
As was mentioned, in this paper, a new approach for novelty and anomaly detection is introduced. However, firstly, the distinction between these two terms should be given, and for this purpose the definitions presented in [12] are used. In [12], two novelty detection learning scenarios were distinguished, namely the static and the dynamic scenarios. In the static scenario [13; 14; 15], the novelty detection problem is considered as a binary classification problem. Thus, a given dataset is composed by only one class and the designed model should be able to identify whether the newly arrived instance is normal or not, as the latter means that the novelty was detected [16; 17]. The dynamic novelty detection, also known as evolving classes, future classes or novel class detection [11; 18; 19], can be formalized as a classification problem with an unknown number of labels. Therefore, the classifier has to determine whether the new instance belongs to one of the currently known classes or not. To be more specific, if the classifier is not sure whether the considered instance belongs to one of the known classes, then this instance should be stored in some external buffer [20; 21]. The instances stored in the mentioned buffer are clustered when their number is sufficient, and later the obtained validated clusters are evaluated as either extensions of some known classes or novelties. It should be noted that as the classes change over time, the classifier must be updated to be able to detect these changes. Thus, there are two possible outcomes of applying a given model for the dynamic novelty detection problem: concept evolution [4] and concept drift [22]. Concept evolution refers to the appearance of novel classes in dynamically updated datasets. Novel concepts should be determined as soon as they appear in data so that they can also be assimilated into the underlying concept for further detection of recurring novel class instances. The term concept drift refers to changes in the conditional distribution of the output or, in other words, target variable given the input features, while the distribution of the input features may stay unchanged [22]. Concept drift indicates that there are extensions of previously established classes, which should be considered for new incoming data instances. In the case of anomaly detection, the data are labelled in two categories, namely the normal and anomalous categories, for example, in studies [23; 24] patients with cancer and fraudulent credit-card transactions were considered as anomalies, respectively. It is assumed that anomalous instances are scarce due to the unbalanced distribution between normal and anomaly classes [25] and, thus, the anomaly detection learning scenario can be formalized as an unbalanced supervised classification problem. Overall, the methods developed for novelty and anomaly detection can be divided into four groups:  
* probabilistic methods [26; 27; 28; 29];
* distance-based methods [30; 31; 32];
* reconstruction-based methods [33; 34; 35; 36];
* deep learning methods [37; 38; 39; 40]. Probabilistic novelty detection approaches are based on the idea that a statistical model can be designed using the training data, and then this model can be applied to estimate whether a new instance (or test sample) belongs to the same distribution or not. There are two basic models to follow when designing a statistical novelty detector: parametric and non-parametric models. Parametric methods assume that the data come from a family of known distributions or are generated from an underlying parametric distribution [41; 42; 43]. The most commonly used form of distribution for continuous data is the Gaussian, but a mixture of other distributions, such as gamma, Poisson or Student's \(t\), can be used for more complex forms of data distribution modelling.