Clustering by transmission learning from data density to label manifold with statistical diffusion

Multi-Source Co-adaptation for EEG-Based Emotion Recognition by Mining Correlation Information

“…(2) based on the designed regression model and the semantic distribution matching between each pair of domains, it not merely provides robustness on loss function but also retains the domain distribution (including local and global) structures and meanwhile maintains a high dependence on the (pseudo)label knowledge of the source domains and the target domain (Zhang et al, 2020a) so as to obtain preferable generalization performance; and (3) through our constructed metric function of correlation, we can make full use of the correlative information among multiple sources and transfer more discriminative knowledge to the target domain. To implement these properties, in the following part, we will detail the objective formulation of the proposed method.…”

“…The method utilizes the correlated knowledge among domains and features by joint l 2,1 − norm and correlation metric regularization and can process high-dimensional, sparse, outliers, and non-i.i.d EEG data at the same time. The designed method has three characteristics, which are integrated into a unified optimization formulation to find an effective emotion recognition model and align the feature distribution between source and target domains: (1) via employing the l 2,1 − norm minimization, a robust loss term is introduced to avoid the influence of noise or outliers in EEG signal, and a sparse regularization term is designed to eliminate over-fitting and a sparse feature subset is selected; (2) based on the designed regression model and the semantic distribution matching between each pair of domains, it not merely provides robustness on loss function but also retains the domain distribution (including local and global) structures and meanwhile maintains a high dependence on the (pseudo)-label knowledge of the source domains and the target domain ( Zhang et al, 2020a ) so as to obtain preferable generalization performance; and (3) through our constructed metric function of correlation, we can make full use of the correlative information among multiple sources and transfer more discriminative knowledge to the target domain. To implement these properties, in the following part, we will detail the objective formulation of the proposed method.…”

An Empirical Study on Optimal the Allocations in Advertising and Operation Innovation on Supply Chain Alliance for Complex Data Analysis

“…Before performing the numerical analysis, we need to reorganize the constraints among the variables to obtain meaningful conclusions. Statistical big data based on advanced product identification can capture the basic relationships among the variables [36][37][38][39][40][41][42][43][44][45]. Subjecting to all constraints being satisfied, it is reasonable to assume the values of some parameters as follows: potential initial market D=1000, original production cost c =10, sensitivity coefficient of market to price k =10, advertising sensitivity coefficient α =0.7, and innovation sensitivity coefficient β =0.065.…”

PPANet: Point‐Wise Pyramid Attention Network for Semantic Segmentation

“…Early studies have developed to detect lanes using mathematical models and traditional computer vision algorithms. For instance, many algorithms have been developed to work on supervised and unsupervised approaches [4][5][6][7]. The current paradigm of research has shifted towards nontraditional machine learning methods, namely, deep learning.…”