Results 231 to 240 of about 20,057 (276)

Independent component analysis by lp-norm optimization

Pattern Recognition, 2018
Abstract In this paper, a couple of new algorithms for independent component analysis (ICA) are proposed. In the proposed methods, the independent sources are assumed to follow a predefined distribution of the form f ( s ) = α exp ( − β | s | p ) and a maximum likelihood estimation is used to separate the sources.
Sungheon Park, Nojun Kwak
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Training Lp norm multiple kernel learning in the primal

Neural Networks, 2013
Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal.
Liang, Zhizheng, Xia, Shixiong, Zhou, Yong, Zhang, Lei   +3 more
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Lp Norm Iterative Sparse Solution for EEG Source Localization

IEEE Transactions on Biomedical Engineering, 2007
How to localize the neural electric activities effectively and precisely from the scalp EEG recordings is a critical issue for clinical neurology and cognitive neuroscience. In this paper, based on the spatial sparse assumption of brain activities, proposed is a novel iterative EEG source imaging algorithm, Lp norm iterative sparse solution (LPISS). In
Peng, Xu, Yin, Tian, Huafu, Chen, Dezhong, Yao   +3 more
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lp-Norm Multiple Kernel Learning

2011
Ziel des Maschinellen Lernens ist das Erlernen unbekannter Konzepte aus Daten. In vielen aktuellen Anwendungsbereichen des Maschinellen Lernens, wie zum Beispiel der Bioinformatik oder der Computer Vision, sind die Daten auf vielfältige Art und Weise in Merkmalsgruppierungen repräsentiert.
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Capped Lp-Norm Graph Embedding for Photo Clustering

Proceedings of the 24th ACM international conference on Multimedia, 2016
Photos are a predominant source of information on a global scale. Cluster analysis of photos can be applied to situation recognition and understanding cultural dynamics. Graph-based learning provides a current approach for modeling data in clustering problems.
Mengfan Tang, Feiping Nie, Ramesh Jain
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Generalized elastic net Lp-norm nonparallel support vector machine

Engineering Applications of Artificial Intelligence, 2020
Abstract Generalized eigenvalue proximal support vector machine (GEPSVM) is the first nonparallel support vector machine. Compared to standard support vector machine (SVM), GEPSVM coped with the “Xor” problem well. In this paper, by defining a generalized elastic net regularization which is the combination of the L2-norm and Lq-norm, we propose a ...
Chun-Na Li, Pei-Wei Ren, Yuan-Hai Shao, Ya-Fen Ye, Yan-Ru Guo   +4 more
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Lp-Norm IDF for Large Scale Image Search

2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013
The Inverse Document Frequency (IDF) is prevalently utilized in the Bag-of-Words based image search. The basic idea is to assign less weight to terms with high frequency, and vice versa. However, the estimation of visual word frequency is coarse and heuristic.
Liang Zheng, Shengjin Wang, Ziqiong Liu, Qi Tian   +3 more
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Lp-norm spherical distribution

Journal of Statistical Planning and Inference, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gupta, A. K., Song, D.
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Autoregressive model in the Lp norm space for EEG analysis

Journal of Neuroscience Methods, 2015
The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification. In real applications, EEGs are inevitably contaminated with unexpected outlier artifacts, and this must be overcome.
Peiyang, Li, Xurui, Wang, Fali, Li, Rui, Zhang, Teng, Ma, Yueheng, Peng, Xu, Lei, Yin, Tian, Daqing, Guo, Tiejun, Liu, Dezhong, Yao, Peng, Xu   +11 more
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ISS in Spatial Lp Norms for Parabolic PDEs

2018
The chapter provides ISS estimates of the solutions of 1-D, parabolic PDEs with respect to boundary and distributed disturbances, which are expressed in weighted spatial \( L^{p} \) norms of the state with \( 1 \le p \le + \infty \). A novel methodology for the derivation of the ISS estimates is presented: the use of an ISS-Lyapunov Functional Under ...
Iasson Karafyllis, Miroslav Krstic
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