Abstract
Multiple kernel clustering algorithms achieve promising performances by exploring the complementary information from kernel matrices corresponding to each data view. Most of the existing methods aim to construct a consensus kernel for the afterward clustering. However, they ignore that the desired kernel is supposed to reveal the cluster structure among samples and thus to be low rank. As a consequence, the corresponding clustering performance could decrease. To address this issue, we propose a low-rank kernel learning approach for multiple kernel clustering. Specifically, instead of regularizing the consensus kernel with low-rank constraints, we use a re-parameterize scheme for the kernel matrix. Meanwhile, the consensus kernel is located in the neighborhood area of the linear combination of base kernels. An alternate optimization strategy is designed to solve the resulting optimization problem. We evaluate the proposed method on 13 benchmark datasets with 9 state-of-the-art algorithms. As is demonstrated by experimental results, our proposed algorithm achieves superior clustering scores against the compared algorithms on the reported popular multiple kernel datasets.
Highlights
Clustering is one of the major research topics regarding semi-supervised and unsupervised learning tasks, which allows the learning models to automatically uncover the patterns and structures of data and categorize these items into different classes
It is based on the assumptions that 1) the features provided by each view are self-sufficient for the clustering task and 2) similar feature patterns lead to the same cluster prediction probabilities
We present an algorithm named multiple kernel k-means with low-rank neighborhood kernel (MKKM-LR) in this paper
Summary
Clustering is one of the major research topics regarding semi-supervised and unsupervised learning tasks, which allows the learning models to automatically uncover the patterns and structures of data and categorize these items into different classes . Representative researches fall into two general types, namely, low-rank representation (LRR) [20] and sparse subspace clustering (SSC) [21] Both of these approaches follow a similar algorithmic routine: first by aligning the representation with the original data, by applying the reconstruction matrix to spectral clustering to acquire the final clustering result. In additional to the simple assumption that the ideal kernel can be learned with linearity, ONKC imposes an alternative kernel which lies in the neighborhood of the linearly combined kernel, acquiring the property of nonlinearity This method expands the searching scope of the possible optimal kernel for clustering. We propose a multiple kernel clustering algorithm by searching the neighborhood of the linear combination of base kernels and re-parameterizing the kernel matrix.
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