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The novelty/anomaly detection in time-series (TS) data is an admired research domain, which is specifically a one-class classification (OCC) task, where only target-class samples are present during training and the samples from other classes are unavailable. The performance of OCC algorithms depends on quality and quantity of features and training samples because all the features/samples are not equally important for target-class representation. The present research focuses on OCC of univariate time-series (UTS) and proposes a novel way to acquire the knowledge of the target-class to ensure its strong separation from the other class samples. Apart from enormous training samples, the large sample length (span) increases the computing complexities together with its innate problem of curse of "dimensionality" (here, sample span is treated as dimension of time-series). In this context, the present article offers a concurrent way of target-class guided sample span reduction and training sample selection for UTS data. Initially, the vector representation is obtained using state-of-the-art dissimilarity-based representation (DBR) techniques and later, a novel target-class supervised sample span reduction algorithm is offered via Eigenspace analysis to obtain the minimal sample span. Furthermore, to select the most promising training samples as target-class representatives, state-of-the-art prototype methods are utilized. Finally, one-class support vector machine (OCSVM), 1-nearest neighbour (1-NN) and isolation forest (IF) are utilized to evaluate the performance of proposed approach. Intensive experiments are performed over the archive of 85 univariate datasets provided by University of California, Riverside (UCR) and University of East Anglia (UEA) (this repository is also known as UCR/UEA archive). [ABSTRACT FROM AUTHOR]

In one-class classification (OCC) tasks, only the target class (class-of-interest (CoI)) samples are well defined during training, whereas the other class samples are totally absent. In OCC algorithms, the high dimensional data adds computational overhead apart from its intrinsic property of curse of dimensionality. For target class learning, conventional dimensionality reduction (DR) techniques are not suitable due to negligence of the unique statistical properties of CoI samples. In this context, the present research proposes a novel target class guided DR technique to extract the eigen knowledge grid that contains the most promising eigenvectors of variance-covariance matrix of CoI samples. In this process the lower and higher eigenvalued eigenvectors are rejected via statistical analysis because the high variance may split the target class itself, whereas the lower variance do not contribute significant information. Furthermore, the identified eigen knowledge grid is utilized to transform high dimensional samples to the lower dimensional eigen subspace. The proposed approach is named as learning target class eigen subspace (LTS-ES) that ensures strong separation of the target class from other classes. To show the effectiveness of transformed lower dimensional eigen subspace, oneclass support vector machine (OCSVM) has been experimented on wide variety of benchmark datasets in presence of: original feature space, transformed features obtained via eigenvectors of approximately 80%-90% cumulative variance, transformed features obtained via knowledge grid and transformed features obtained via eigenvectors of approximately 50% cumulative variance. Finally, a new performance measure parameter called stability factor is introduced to validate the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]