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This short note presents upper bounds of the expectations of the largest singular values/eigenvalues of various types of random tensors in the non-asymptotic sense. For a standard Gaussian tensor of size $n_1timescdotstimes n_d$, it is shown that the expectation of its largest singular value is upper bounded by $sqrt {n_1}+cdots+sqrt {n_d}$. For the expectation of the largest $ell^d$-singular value, it is upper bounded by $2^{frac{d-1}{2}}prod_{j=1}^{d}n_j^{frac{d-2}{2d}}sum^d_{j=1}n_j^{frac{1}{2}}$. We also derive the upper bounds of the expectations of the largest Z-/H-($ell^d$)/M-/C-eigenvalues of symmetric, partially symmetric, and piezoelectric-type Gaussian tensors, which are respectively upper bounded by $dsqrt n$, $dcdot 2^{frac{d-1}{2}}n^{frac{d-1}{2}}$, $2sqrt m+2sqrt n$, and $3sqrt n$.
We consider a Gaussian rotationally invariant ensemble of random real totally symmetric tensors with independent normally distributed entries, and estimate the largest eigenvalue of a typical tensor in this ensemble by examining the rate of growth of
The hierarchical SVD provides a quasi-best low rank approximation of high dimensional data in the hierarchical Tucker framework. Similar to the SVD for matrices, it provides a fundamental but expensive tool for tensor computations. In the present wor
We show that for an $ntimes n$ random symmetric matrix $A_n$, whose entries on and above the diagonal are independent copies of a sub-Gaussian random variable $xi$ with mean $0$ and variance $1$, [mathbb{P}[s_n(A_n) le epsilon/sqrt{n}] le O_{xi}(epsi
This paper introduces the functional tensor singular value decomposition (FTSVD), a novel dimension reduction framework for tensors with one functional mode and several tabular modes. The problem is motivated by high-order longitudinal data analysis.
We precisely determine the SDP value (equivalently, quantum value) of large random instances of certain kinds of constraint satisfaction problems, ``two-eigenvalue 2CSPs. We show this SDP value coincides with the spectral relaxation value, possibly i