Results 41 to 50 of about 1,217,124 (142)
Improved Spectral-Norm Bounds for Clustering [PDF]
, 2012 Aiming to unify known results about clustering mixtures of distributions under separation conditions, Kumar and Kannan[2010] introduced a deterministic condition for clustering datasets. They showed that this single deterministic condition encompasses many previously studied clustering assumptions.
Pranjal Awasthi, Or Sheffet
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New space-time block codes from spectral norm.
PLoS ONE, 2019 Current research proposes a natural environment for space-time codes and a new design criterion is obtained for space-time block codes in multi-antenna communication channels.
Carlos A R Martins +2 more
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Approximation Properties of Chebyshev Polynomials in the Legendre Norm
Mathematics, 2021 In this paper, we present some important approximation properties of Chebyshev polynomials in the Legendre norm. We mainly discuss the Chebyshev interpolation operator at the Chebyshev–Gauss–Lobatto points.
Cuixia Niu +3 more
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On the spectral norms of pseudo-wigner and related matrices [PDF]
2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2017 We investigate the spectral norms of symmetric $N \times N$ matrices from two pseudo-random ensembles. The first is the pseudo-Wigner ensemble introduced in "Pseudo-Wigner Matrices" by Soloveychik, Xiang and Tarokh and the second is its sample covariance-type analog defined in this work.
Ilya Soloveychik, Vahid Tarokh
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Boolean functions with small spectral norm, revisited [PDF]
, 2018 We show that if f is a Boolean function on Fn 2 with spectral norm at most M then there is some L exp(M3+o(1) ) and subspaces V1,..., VL such that f = i ±1Vi
Sanders, Tom, TOM SANDERS
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Deviation inequalities for the spectral norm of structured random matrices [PDF]
We study the deviation inequality for the spectral norm of structured random matrices with non-gaussian entries. In particular, we establish an optimal bound for the p-th moment of the spectral norm by transfering the spectral norm into the suprema of ...
Dai, Guozheng, Su, Zhonggen
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Clustering with Spectral Norm and the k-Means Algorithm [PDF]
2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010 There has been much progress on efficient algorithms for clustering data points generated by a mixture of $k$ probability distributions under the assumption that the means of the distributions are well-separated, i.e., the distance between the means of any two distributions is at least $Ω(k)$ standard deviations.
Amit Kumar 0001, Ravindran Kannan
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On the dynamics of sup-norm non-expansive maps [PDF]
, 2005 We present several results for the periods of periodic points of sup-norm non-expansive maps. In particular, we show that the period of each periodic point of a sup-norm non-expansive map $f\colon M\to M$, where $M\subset \mathbb{R}^n$, is at most ...
Lemmens, Bas +3 more
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Multiple Spectral-Spatial Representation Based on Tensor Decomposition for HSI Anomaly Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 To exploit the spectral-spatial information of hyperspectral image (HSI) and achieve higher the detection accuracy, a novel multiple spectral-spatial representation based on tensor decomposition method is proposed for HSI anomaly detection (AD) in this ...
Yujian Wang +5 more
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A Note on Spectrally Dominant Norms
Journal of Algebra, 2000 Let \(A\) be a finite-dimensional associative complex algebra. A vector space norm on it is called stable if there exists a positive constant \(\sigma\) such that \(\|x^n\|\leq\sigma\|x\|^n\) for every \(x\in A\) and all \(n\). It is known that stable norms on \(A\) are spectrally dominant (i.e., for every \(x\in A\), the spectral radius of \(x\leq\|x\|
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