30 research outputs found
Unified Spectral Clustering with Optimal Graph
Spectral clustering has found extensive use in many areas. Most traditional
spectral clustering algorithms work in three separate steps: similarity graph
construction; continuous labels learning; discretizing the learned labels by
k-means clustering. Such common practice has two potential flaws, which may
lead to severe information loss and performance degradation. First, predefined
similarity graph might not be optimal for subsequent clustering. It is
well-accepted that similarity graph highly affects the clustering results. To
this end, we propose to automatically learn similarity information from data
and simultaneously consider the constraint that the similarity matrix has exact
c connected components if there are c clusters. Second, the discrete solution
may deviate from the spectral solution since k-means method is well-known as
sensitive to the initialization of cluster centers. In this work, we transform
the candidate solution into a new one that better approximates the discrete
one. Finally, those three subtasks are integrated into a unified framework,
with each subtask iteratively boosted by using the results of the others
towards an overall optimal solution. It is known that the performance of a
kernel method is largely determined by the choice of kernels. To tackle this
practical problem of how to select the most suitable kernel for a particular
data set, we further extend our model to incorporate multiple kernel learning
ability. Extensive experiments demonstrate the superiority of our proposed
method as compared to existing clustering approaches.Comment: Accepted by AAAI 201
Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT
[EN] In the cognitive computing of intelligent industrial Internet of Things, clustering is a fundamental machine learning problem to exploit the latent data relationships. To overcome the challenge of kernel choice for nonlinear clustering tasks, multiple kernel clustering (MKC) has attracted intensive attention. However, existing graph-based MKC methods mainly aim to learn a consensus kernel as well as an affinity graph from multiple candidate kernels, which cannot fully exploit the latent graph information. In this article, we propose a novel pure graph-based MKC method. Specifically, a new graph model is proposed to preserve the local manifold structure of the data in kernel space so as to learn multiple candidate graphs. Afterward, the latent consistency and selfishness of these candidate graphs are fully considered. Furthermore, a graph connectivity constraint is introduced to avoid requiring any postprocessing clustering step. Comprehensive experimental results demonstrate the superiority of our method.This work was supported in part by Sichuan Science and Technology Program under Grant 2020ZDZX0014 and Grant 2019ZDZX0119 and in part by the Key Lab of Film and TV Media Technology of Zhejiang Province under Grant 2020E10015.Ren, Z.; Mukherjee, M.; Lloret, J.; Venu, P. (2021). Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT. IEEE Transactions on Industrial Informatics. 17(4):2956-2963. https://doi.org/10.1109/TII.2020.3010357S2956296317
Reliability Evaluation of Wind Turbine Systems’ Components
The increasing use of wind generation requests modifications in the electric power systems planning conception, because it includes one more uncertainty component, which needs to be studied properly and modeled. Understanding the failures rates and downtimes of wind turbines is difficult not only because of the considerable range of designs and sizes that are now in service worldwide but also since studies are conducted independently under various operating conditions in different countries. The fault tree method (FTA) has been used to study the reliability of many different power generation systems. This paper now applies that method to a wind turbine system to estimate the reliability of wind turbines. In the implementations, several types of wind turbines were considered in order to analyze the system’s reliability. The effectiveness of the proposed method is revealed through several case studies
Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition
Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm