Titlebook: Advances in Knowledge Discovery and Data Mining; 26th Pacific-Asia Co Jo?o Gama,Tianrui Li,Fei Teng Conference proceedings 2022 The Editor(

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Convergence and Applications of ADMM on the Multi-convex Problems, delivering an impressive performance in areas such as nonnegative matrix factorization and sparse dictionary learning, there remains a dearth of generic work on proposed ADMM with a convergence guarantee under mild conditions. In this paper, we propose a generic ADMM framework with multiple couple

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Quantum Entanglement Inspired Correlation Learning for?Classificationion between quantum entangled systems often surpasses that between classical systems, quantum information processing methods show superiority that classical methods do not possess. In this paper, we study the virtue of entangled systems and propose a novel classification algorithm called Quantum Ent

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Uniform Evaluation of?Properties in?Activity Recognition AR is durative and can be correct in a period and incorrect in another one. Therefore, it is fundamental to extend the correctness vocabulary and to formalize a new evaluation system including these extensions. Even in similar areas, few empirical attempts are proposed which are confronted with the

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Effect of?Different Encodings and?Distance Functions on?Quantum Instance-Based Classifiersng, a paradigm for computing making use of quantum theory. Quantum computing can empower machine learning with theoretical properties allowing to overcome the limitations of classical computing. The translation of classical algorithms into their quantum counter-part is not trivial and hides many dif

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Attention-to-Embedding Framework for?Multi-instance Learningere learning objects are bags containing various numbers of instances. Two key issues of this work are to extract relevant information by determining the relationship between the bag and its instances, and to embed the bag into a new feature space. To respond to these problems, a network with the po

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Multi-instance Embedding Learning Through High-level Instance Selection instance selection transform bags into a single-instance space. However, they may select weak representative instances due to the ignorance of the internal bag structure. In this paper, we propose the multi-instance embedding learning through high-level instance selection (MIHI) algorithm with two

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High Average-Utility Itemset Sampling Under Length Constraints widely demonstrated in many real world applications. The traditional algorithms return the set of all patterns with a utility above a minimum utility threshold which is difficult to fix, while top-k algorithms tend to lack of diversity in the produced patterns. We propose an algorithm named . to sa

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Divide and?Imitate: Multi-cluster Identification and?Mitigation of?Selection Biasing data is biased, however, that bias will be transferred to the model and remains undetected as the performance is validated on a test set drawn from the same biased distribution. Existing strategies for selection bias identification and mitigation generally rely on some sort of knowledge of the b