نتایج جستجو برای: sparseness constraint

تعداد نتایج: 79838  

Journal: :IEEE Transactions on Pattern Analysis and Machine Intelligence 2003

Journal: :EURASIP J. Audio, Speech and Music Processing 2013
Jen-Tzung Chien Hsin-Lung Hsieh

Nonnegative matrix factorization (NMF) is developed for parts-based representation of nonnegative signals with the sparseness constraint. The signals are adequately represented by a set of basis vectors and the corresponding weight parameters. NMF has been successfully applied for blind source separation and many other signal processing systems. Typically, controlling the degree of sparseness a...

Journal: :Journal of Machine Learning Research 2015
Qi Yan Jieping Ye Xiaotong Shen

In multi-response regression, pursuit of two different types of structures is essential to battle the curse of dimensionality. In this paper, we seek a sparsest decomposition representation of a parameter matrix in terms of a sum of sparse and low rank matrices, among many overcomplete decompositions. On this basis, we propose a constrained method subject to two nonconvex constraints, respectiv...

Journal: :Network 2001
B Willmore D J Tolhurst

It is often suggested that efficient neural codes for natural visual information should be 'sparse'. However, the term 'sparse' has been used in two different ways--firstly to describe codes in which few neurons are active at any time ('population sparseness'), and secondly to describe codes in which each neuron's lifetime response distribution has high kurtosis ('lifetime sparseness'). Althoug...

Journal: :IEEE Access 2021

Due to the low spatial resolution of sensors, hyperspectral images contain mixed pixels. The purpose unmixing is decompose pixels into a series endmembers and abundance fractions. In order improve performance nonlinear algorithm for images, method, i.e., constrained multi-layer kernel non-negative matrix factorization (AC-MLKNMF), presented. Firstly, MLKNMF presented iteratively structure, then...

2011
Qiang Wu Liqing Zhang Andrzej Cichocki

Higher order tensor model has been seen as a potential mathematical framework to manipulate the multiple factors underlying the observations. In this paper, we propose a flexible two stage algorithm for K-mode Convolutive Nonnegative Tucker Decomposition (K-CNTD) model by an alternating least square procedure. This model can be seen as a convolutive extension of Nonnegative Tucker Decomposition...

Journal: :Annals of Pure and Applied Logic 2005

Journal: :Remote Sensing 2017
Risheng Huang Xiaorun Li Liaoying Zhao

Abstract: Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 and L2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may posses...

Journal: :Optics letters 2005
Ivica Kopriva

Single-frame multichannel blind deconvolution is formulated by applying a bank of Gabor filters to a blurred image. The key observation is that spatially oriented Gabor filters produce sparse images and that a multichannel version of the observed image can be represented as a product of an unknown nonnegative sparse mixing vector and an unknown nonnegative source image. Therefore a blind-deconv...

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