… For the multi-kernel clustering of k-means, MKKM directly adds a multi-kernel learning
method to k-means, while RMKKM imposes ℓ 21 norm constraints on noise items on the basis of …

… Since the performance of the majority of the methods in the aforementioned and k-means
algorithm depends on initialization, we repeat the feature selection and k-means 10 times and …

… usually prefer to work with sparse graphs without self-loops, … the eigenvectors of L n (ie, q
is the identity function), and no … 5.1, we show the result of k-means directly on a set of artificial …

… For example, SSC imposes l 1 norm on Z to obtain a sparse structure; LRR seeks low-rank
coefficient matrix by minimizing the nuclear norm. The multi-subspace representation (MSR) […

… Value Q represents the number of classes in the system, and M j total number of … It represents
the generalization of the K-means clustering in function of finding the best representation …

… Q of k centers, \(\mathrm {far}(P,Q)= \max _{p\in P} \min _{q\in … Why do we need coresets, if
gradient descent provides a sparse … When the query X is a set of centers as in k-means, or a …

… We consider the community detection problem in a sparse |$q$|-uniform hypergraph |$G$|⁠…
especially for |$k$|-means. Indeed, as in [68], the performance of |$k$|-means can be related …

… and concatenate them for sparse representation; then design … mixture k-means to model
speaker corresponding to sparse feature… which approximates the hearing law of human cochlea, …

… Then, we use the K-means and Elbow method to automate choosing the optimal class number.
After that, we apply Multiclass-EM as a robust probability density estimator to find the best …

… 0 and 2, it can be asserted that \(0 = \lambda _{0}\leqq \cdots \leqq \lambda _{n-1} \leqq 2\). …
Then, the complexity of WSCA for a sparse network can be further reduced to \(O(w^{2.376}n…