A Proofs A.1 Proof of Proposition 4.1 Proof

The first lemma is Lemma 3 in [24]. Hermitian matrix and let B be a Hermitian perturbation. To apply Lemma A.1, we must study the relationship between minimum eigenvalue gap of By Lemma A.2, we have (p 1) (p 1) ( p 1) Then, by the proof of Theorem 5.2, we have null null null l null x, y, null H (p 1) (p 1) ( p 1) (p 1) ( p 1) (p 1) (p 1) (p 1) ( p 1) ( p 1) ( p 1) (p 1) (p 1) (p 1) (p 1) (p 1) A.5 The Optimization of SimpleMKKM SimpleMKKM aims to solve the following kernel alignment-based optimization problem: min Assume that the number of iterations is T . Table 4: Large-scale datasets used in the experiments Dataset Samples View Clusters NUSWIDE 30000 5 31 A wA 30475 6 50 MNIST 60000 3 10 YtVideo 101499 5 31 B.2 Clustering Performance with Different Numbers of Landmarks As seen, as the number of landmarks increases, the ACC of the proposed method is approaching SimpleMKKM, and tends to be stable. It shows that we don't need too many landmarks To verify the assumptions about the eigenvalues of the empirical kernel matrix in Theorem 5.2, we To give more empirical studies of the proposed method, we conduct additional experiments on three classic algorithms, i.e., average multiple kernel The results are reported in the following three tables.