Spectral clustering (SC) has become one of the most popular clustering methods. Given an affinity matrix, SC explores its spectral-graph structure to partition data into disjoint meaningful groups. However, in many applications, there are multiple potentially useful features and thereby multiple affinity matrices. For applying spectral clustering to such cases, these affinity matrices must be aggregated into a single one. Unfortunately, affinity measures based on different features could have different characteristics. Some are more effective than others. We propose a multi-affinity spectral clustering (MASC) algorithm which extends the SC algorithm with multiple affinities available. By automatically adjusting the weights of affinity matrices, MASC is more immune to ineffective affinities and irrelevant features. This makes the choice of similarity or distance-metric measures for clustering less crucial. Experiments show that MASC is effective in simultaneous clustering and feature fusion, thus maintaining robustness of SC for multi-affinity clustering problems. Index Terms — spectral clustering, affinity matrix, multiple kernel learning. 1
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