Search Results for author:
Found 6 papers, 1 papers with code
Accelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scaling
However, FCM and its many accelerated variants have low efficiency in the mid-to-late stage of the clustering process.
Multi-Prototypes Convex Merging Based K-Means Clustering Algorithm
Specifically, CM can obtain the optimal merging and estimate the correct k. By integrating these two techniques with K-Means algorithm, the proposed MCKM is an efficient and explainable clustering algorithm for escaping the undesirable local minima of K-Means problem without given k first.
Fast Newton method solving KLR based on Multilevel Circulant Matrix with log-linear complexity
Specifically, by approximating the kernel matrix with an MCM, the storage space is reduced to $O(n)$, and further approximating the coefficient matrix of the Newton equation as MCM, the computational complexity of Newton iteration is reduced to $O(n \log n)$.
Fast Kernel k-means Clustering Using Incomplete Cholesky Factorization
The key idea of the proposed kernel $k$-means clustering using incomplete Cholesky factorization is that we approximate the entire kernel matrix by the product of a low-rank matrix and its transposition.
Stochastic Variance Reduction Gradient for a Non-convex Problem Using Graduated Optimization
Graduated Optimization Algorithm (GOA) is a popular heuristic method to obtain global optimums of nonconvex problems through progressively minimizing a series of convex approximations to the nonconvex problems more and more accurate.
Sparse Algorithm for Robust LSSVM in Primal Space
Then approximating the kernel matrix by a low-rank matrix and smoothing the loss function by entropy penalty function, we propose a convergent sparse R-LSSVM (SR-LSSVM) algorithm to achieve the sparse solution of primal R-LSSVM, which overcomes two drawbacks of LSSVM simultaneously.
