# Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
## 2 Related Work  
In this section, we have introduced an unsupervised novelty detection method related to the proposed method. Recent research suggests unsupervised techniques, such as OC-SVM, one-class neural network (OC-NN), clustering based and reconstruction error based and so on. The relatively small percentage of total data is novelty and class imbalance problems can occur because of divergence of normal and novelty sample ratios. In this case, traditional classification methods are not well suited for discovering novelty. OC-SVM is the semi-supervised approach that is used for a classifier based on building a prediction model from a normal dataset [20; 21; 22; 23]. It is an extension of SVM for an unlabeled dataset that was introduced first by [PERSON] et al. [23] and usage of one-class SVM in unsupervised mode is rising due to the most data is not labeled in the practice. In this case, all data is considered normal and used for the training of OC-SVM. The disadvantage of this method is that if the training data contains abnormal or novelty, the novelty detection classification model does not work well because the decision boundary (the normal boundary) created by OC-SVM shifts toward outliers. [PERSON] et al. showed an Enhanced OC-SVM which is the Robust OC-SVM and the eta-SVM together to eliminate these deficiencies [20]. Robust OC-SVM focused on slack variables that are far from the centroid. They are dropped from the minimization objective because of these points, the decision boundary will be wrong. [PERSON] et al. proposed a hybrid model that combines a deep belief network for feature extraction and OC-SVM for unsupervised novelty detection--named DBN-1 SVM--that solves the curse of the dimensionality problem by reducing dimensionality using a deep belief network [12]. Another popular novelty detection method is principal component analysis (PCA) based methods. PCA is a dimension reduction technique in which the direction with the largest projected variance is called the first principal component. The orthogonal direction that captures the second largest projected variance is called the second principal component and so on [24]. Although in most cases, PCA is used for dimensionality reduction purpose, it is also used to detect novelty. [PERSON] et al. introduced to diagnose outliers by the first principal component score and its orthogonal distance of each data points. Regular observations have both a small orthogonal distance and small PCA score [14]. [PERSON] proposes kernel PCA for novelty detection which is a non-linear extension of PCA. In kernel PCA, input spaces are mapped into higher-dimensional space before performing PCA using a kernel function. In [PERSON]'s proposed method, Gaussian kernel function and reconstruction error in feature space are used [15]. Using the covariance matrix to PCA is sensitive to the outlier. [PERSON] et al. proposed a Robust PCA based novelty detection approach using the correlation matrix instead of the covariance matrix to calculate the principal component scores [25]. In recent studies, hybrid techniques are being suggested that making novelty detection as a deep autoencoder is combined with other methods. Deep autoencoder is a neural network that is trained to attempt to copy its input to its output. [PERSON] et al. proposed a deep neural network with seven layers for voxelwise detection of cerebral microbleeds. They used sparse autoencoder with 4 hidden layers for dimensionality reduction and softmax layer for classification [26]. Also, [PERSON] et al. proposed a deep stacked autoencoder based multi-class classification approach to the image dataset. The first dimensionality is reduced using deep stacked autoencoder and then softmaxlayer is used as a classifier [27]. [PERSON] et al. propose a reconstruction based outlier detection method that has 2 steps including discriminative labeling and reconstruction learning [28].