## 1 Introduction  
Identification of histone modification [1] from high-throughput sequencing (HTS) datasets is important for the following reasons:  
* Histone modification contributes to various functional genomic features, including transcription [2] and alteration of chromatin structures [3]. * In contrast to variable gene expression, histone modification is more stable [4]; thus, it may be used to characterize the state of the genome. * In contrast to DNA methylation, which has only two states, methylated or not, histones are modified in various ways. Thus, histone modification is related to a more detailed functionality of the genome [5]. * Since histone modification may activate or suppress transcription, combinations of various histone modifications may have more complicated transcriptional roles [6]. Despite the importance of histone modification, gene expression and DNA methylation have instead been the focus of genomic studies for the following reasons. * To identify histone modification throughout the genome, the detection of antibody binding to the entire genome is required [7]. In contrast, gene expression can be detected only if transcription start sites are sequenced. * In contrast to gene expression that can be measured only when exons are considered or DNA methylation that is meaningful only if promoter regions are considered, specific regions of the genome cannot be used due to limited knowledge of the position-specific functionality of histone modification [8]. * Because of these two reasons, HTS for histone modification requires more depth, which is both time-consuming and expensive [9]. * Similarly, the number of datasets and computational resources required to identify histone modification is greater than that required to measure gene expression and DNA methylation [10]. Possibly because of its unpopularity, fewer methods specific to histone modification have been developed. In general, histone modification is measured to identify proteins that can bind to specific histone modifications. After fixing the binding of proteins to histone modifications, the remaining part without histone modification is digested and removed. Only the DNA sequences to which proteins bind remain and are mapped to the genome sequence. Therefore, the input datasets are profiles of the amount of proteins that bind to DNA sequences where specific histone modifications occur. This technology is called ChIP-seq. Because histone modification is often measured using chromatin immunoprecipitation sequencing (ChIP-seq) technology to identify protein binding sites on DNA [9], so-called peak-call programs [11] developed to process protein binding to DNA via ChIP-seq have often been used to process HTS datasets used for histone modification. However, because histone modifications are hardly regarded as distributed over the genome by forming peaks, these peak-calling algorithms are not guaranteed to identify histone modification. For example, most methods employ binomial distributions to identify the amount of histone modification over the genome [12], but we are unsure if these methods are suitable. A more flexible method that does not assume a specific distribution of histone modification over the whole genome is required. To fulfill this requirement, the method of tensor decomposition (TD)- and principal component analysis (PCA)-based unsupervised feature extraction (FE) with optimized standard deviation (SD) that was successfully applied to gene expression [13] and DNA methylation [14] was tested to determine if histone modification could also be identified. In contrast to two previous studies [13; 14] where PC or SVVs obey the Gaussian distribution, which is assumed in the null hypothesis after SD optimization, in our method, PC and SVVs follow a mixed Gaussian distribution. Nevertheless, empirically, SD optimization allows the correct identification of histone modification to some extent, even when compared to various state-of-the-art methods. The following is the structure of the rest of this manuscript.