TY - GEN A1 - Gao, Yunlong A1 - Lin, Tingting A1 - Zhang, Yisong A1 - Luo, Sizhe A1 - Nie, Feiping T1 - Robust principal component analysis based on discriminant information T2 - IEEE Transactions on Knowledge and Data Engineering N2 - Recently, several robust principal component analysis (RPCA) models were presented to enhance the robustness of PCA by exploiting the robust norms as their loss functions. But an important problem is that they have no ability to discriminate outliers from correct samples. To solve this problem, we propose a robust principal component analysis based on discriminant information (RPCA-DI). RPCA-DI disentangles the robust PCA with a two-step fashion: the identification and the processing of outliers. To identity outliers, a sample representation model based on entropy regularization is constructed to analyze the membership of data belonging to the principal component space(PC) and its orthogonal complement(OC), the discriminative information of data will be extracted based on measuring the differences of retained information on PC(or OC) of data. By this way, we can discriminate correct samples when we deal with outliers, which is more reasonable for robustness learning respective to previous works. In the noise processing step, in addition to considering the levels of noise, the resistance of the sample points to noise is also considered to prevent overfitting, thereby improving the generalization performance of RPCA-DI. Finally, an iterative algorithm is designed to solve the corresponding model. Compared with some state-of-art RPCA methods on artificial datasets, UCI datasets and face databases that verifies the effectiveness of our proposed algorithm. KW - Principal component analysis KW - entropy regularization KW - robustness Y1 - 2023 U6 - https://doi.org/10.1109/TKDE.2021.3093447 SN - 1558-2191 SN - 1041-4347 VL - 35 IS - 2 SP - 1991 EP - 2003 PB - IEEE ER - TY - GEN A1 - Berti, Alessandro A1 - Koren, Istvan A1 - Adams, Jan Niklas A1 - Park, Gyunam A1 - Knopp, Benedikt A1 - Graves, Nina A1 - Rafiei, Majid A1 - Liß, Lukas A1 - Tacke Genannt Unterberg, Leah A1 - Zhang, Yisong A1 - Schwanen, Christopher A1 - Pegoraro, Marco A1 - Aalst, Wil van der T1 - OCEL (Object-Centric Event Log) 2.0 Specification T2 - arXiv N2 - Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for more extensive process analyses while remaining in an easily exchangeable format. In contrast to the first OCEL standard, it can depict changes in objects, provide information on object relationships, and qualify these relationships to other objects or specific events. Compared to XES, it is more expressive, less complicated, and better readable. OCEL 2.0 offers three exchange formats: a relational database (SQLite), XML, and JSON format. This OCEL 2.0 specification document provides an introduction to the standard, its metamodel, and its exchange formats, aimed at practitioners and researchers alike. Y1 - 2024 UR - https://arxiv.org/abs/2403.01975 SP - 1 EP - 49 ER -