Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral clustering for single and multiple views has been the common solution. Despite its good clustering performance, it has a major limitation: it requires an extra step of ...
View more references Cited by (8) Weighted ensemble clustering with multivariate randomness and random walk strategy 2024, Applied Soft Computing Citation Excerpt : EAC [41]: Evidence accumulation clustering. ND-Ward-E(KT) [42]: In-tree-based clustering via the distance ensemble and the kernel ...
Wang M, Song X, Liu Y, Xu C (2022) Neural tangent kernel k-means clustering. J Comput Appl 42:3330 Google Scholar Nguyen TV, Wong RK, Hegde C (2021) Benefits of jointly training autoencoders: an improved neural tangent kernel analysis. IEEE Trans Inf Theory 67(7):4669–4692 Article...
Starting with a set of base kernels, i.e., Mercer’s conditions are satisfied, we assume kernel 𝒦K is a weighted combination of those kernel matrices by: 𝒦=∑𝑘=1𝑚𝜎𝑘𝐾𝑘,K=∑k=1mσkKk, (1) where there are m kernels and 𝜎𝑘σk is the weight applied to ...
we propose a novel multi-omics cancer subtyping method based on Multi-Kernel Partition Alignment Subspace clustering (MKPAS). Given multiple omics datasets, MKPAS first uses multiple kernel functions to generate kernel matrices as the input of multi-view subspace learning model. Second, it uses sub...
As shown in Figure 13., the accuracy of SVM and LDA is greatly improved compared with that of the traditional unsupervised clustering algorithm. From the data point of view, the accuracy of the two classification methods has been improved to some extent after the introduction of the Gabor image...
This study uses the normalized local weighted regression (LWR) algorithm to estimate the coefficients of the 1st to 10th order MVAR, and selects the optimal model orderpby minimizing the Akaike Information Criterion (AIC). The smaller the AIC value, the better the model fits the data, and at...
Considering the impact of high dimensional data redundancy and noise interference on multiview subspace clustering, a robust multiview subspace clustering method based on multi-kernel low redundancy representation learning was proposed. Firstly, by analyzing and revealing th...
2) The noise from corrupted data or occlusion may destroy the block-diagonal structures of the affinity matrices they obtained, which will affect the clustering performance when using spectral clustering. In this work, to solve the above problems, we propose an automatic weighted multikernel ...
Eventually, the estimated weighted matrix Bˆ can be calculated as follow: (6) Bˆ = HT ( 1 C I + HHT )−1 T, (7) where I is the identity matrix. The solution of the output function fL(x) for x can be obtained as follows: fL(x) = h(x)HT 1 C I + HHT −1 T....