16 citations · 22 across the 7 of their papers we have counts for
7 papers
Accelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scaling
Dong Li, Shuisheng Zhou, Witold Pedrycz
Fuzzy C-Means (FCM) is a widely used clustering method. However, FCM and its many accelerated variants have low efficiency in the mid-to-late stage of the clustering process. In th…
Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm
Dong Li, Shuisheng Zhou, Tieyong Zeng +1
K-Means algorithm is a popular clustering method. However, it has two limitations: 1) it gets stuck easily in spurious local minima, and 2) the number of clusters k has to be given…
Fast Newton method solving KLR based on Multilevel Circulant Matrix with log-linear complexity
Junna Zhang, Shuisheng Zhou, Cui Fu +1
Kernel logistic regression (KLR) is a conventional nonlinear classifier in machine learning. With the explosive growth of data size, the storage and computation of large dense kern…
Unified SVM Algorithm Based on LS-DC Loss
Zhou Shuisheng, Zhou Wendi
Over the past two decades, support vector machine (SVM) has become a popular supervised machine learning model, and plenty of distinct algorithms are designed separately based on d…
Fast Kernel k-means Clustering Using Incomplete Cholesky Factorization
Li Chen, Shuisheng Zhou, Jiajun Ma
Kernel-based clustering algorithm can identify and capture the non-linear structure in datasets, and thereby it can achieve better performance than linear clustering. However, comp…
Stochastic Variance Reduction Gradient for a Non-convex Problem Using Graduated Optimization
Li Chen, Shuisheng Zhou, Zhuan Zhang
In machine learning, nonconvex optimization problems with multiple local optimums are often encountered. Graduated Optimization Algorithm (GOA) is a popular heuristic method to obt…
Sparse Algorithm for Robust LSSVM in Primal Space
Li Chen, Shuisheng Zhou
As enjoying the closed form solution, least squares support vector machine (LSSVM) has been widely used for classification and regression problems having the comparable performance…