In this paper, we resolve this problem by introducing the graph Laplacian of the observed data as a regularizer when optimizing the kernel matrix with respect ...

To learn the kernel matrix in a nonparametric setup, it is often convenient to cast the kernel learning problem into an optimization problem that includes the ...

Existing methods all assume certain parametric form of the kernel;. • Or a linear combination of provided kernels. • This work focus on non-parametric ...

To learn the kernel matrix in a nonparametric setup, it is often convenient to cast the kernel learning problem into an optimization problem that includes the ...

In this paper, we resolve this problem by introducing the graph Laplacian of the observed data as a regularizer when optimizing the kernel matrix with respect ...

Kernel plays an important role in many machine learning techniques. Many kernel learning algorithms (e.g., multiple kernel learning) have to assume ...

The extensive evaluation on clustering with pairwise constraints shows that the proposed nonparametric kernel learning method is more effective than other state ...

In this paper, we resolve this problem by introducing the graph Laplacian of the observed data as a regularizer when optimizing the kernel matrix with respect ...

Abstract For existing kernel learning based semi-supervised clustering algorithms, it is generally difficult to scale well.

Mar 23, 2022 · We introduce a new constrained clustering algorithm that jointly clusters data and learns a kernel in accordance with the available pairwise ...