6,961 research outputs found

    Nonparametric Estimation of Multi-View Latent Variable Models

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    Spectral methods have greatly advanced the estimation of latent variable models, generating a sequence of novel and efficient algorithms with strong theoretical guarantees. However, current spectral algorithms are largely restricted to mixtures of discrete or Gaussian distributions. In this paper, we propose a kernel method for learning multi-view latent variable models, allowing each mixture component to be nonparametric. The key idea of the method is to embed the joint distribution of a multi-view latent variable into a reproducing kernel Hilbert space, and then the latent parameters are recovered using a robust tensor power method. We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters. Thus, our non-parametric tensor approach to learning latent variable models enjoys good sample and computational efficiencies. Moreover, the non-parametric tensor power method compares favorably to EM algorithm and other existing spectral algorithms in our experiments

    Kinetic Ballooning Mode Under Steep Gradient: High Order Eigenstates and Mode Structure Parity Transition

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    The existence of kinetic ballooning mode (KBM) high order (non-ground) eigenstates for tokamak plasmas with steep gradient is demonstrated via gyrokinetic electromagnetic eigenvalue solutions, which reveals that eigenmode parity transition is an intrinsic property of electromagnetic plasmas. The eigenstates with quantum number l=0l=0 for ground state and l=1,2,3…l=1,2,3\ldots for non-ground states are found to coexist and the most unstable one can be the high order states (l≠0l\neq0). The conventional KBM is the l=0l=0 state. It is shown that the l=1l=1 KBM has the same mode structure parity as the micro-tearing mode (MTM). In contrast to the MTM, the l=1l=1 KBM can be driven by pressure gradient even without collisions and electron temperature gradient. The relevance between various eigenstates of KBM under steep gradient and edge plasma physics is discussed.Comment: 6 pages, 6 figure
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