Results 211 to 220 of about 5,042 (258)
Some of the next articles are maybe not open access.
Independent component analysis by lp-norm optimization
Pattern Recognition, 2018Abstract 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
openaire +1 more source
Lp Norms and the Sinc Function.
Am. Math. Mon., 2010It’s everywhere! It’s everywhere! … In this note we give elementary proofs of some of the striking asymptotic properties of the p-norm of the ubiquitous sinc function. Based on experimental evidence we conjecture some enticing further properties of the p-norm as a function of p. See, for example, http://www.carma.newcastle.edu.au/~jb616/oscillatory.pdf.
David Borwein +2 more
openaire +1 more source
Vector and matrix LP norms in polarimetric radar filtering
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012The paper addresses multi-channel complex image filtering. It provides regularization cost functions associated to non-conventional vector and matrix iv norms for promoting geometry properties. The approach is shown to be efficient for filtering PolSAR images.
Atto, Abdourrahmane +3 more
openaire +2 more sources
Lp norm design of stack filters.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1999This paper addresses the problem of designing optimal stack filters by employing an Lp norm of the error between the desired signal and the estimated one. It is shown that the Lp norm can be expressed as a linear function of the decision errors at the binary levels of the filter.
C. Emanuel Savin +2 more
openaire +2 more sources
Combinatorial Search for the Lp-Norm Principal Component of a Matrix
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019We study Lp-norm Principal-Component Analysis (Lp-PCA) of a matrix. For p = 2 (standard PCA), the problem can be solved with standard Singular-Value Decomposition (SVD). For p = 1 (L1-PCA), the problem was recently solved exactly and approximately with efficient iterative algorithms. For general values of p, the exact solution to Lp-PCA remains to date
Dimitris G. Chachlakis +1 more
openaire +1 more source
Fast Time Sequence Indexing for Arbitrary Lp Norms [PDF]
, 2018 Fast indexing in time sequence databases for similarity searching has attracted a lot of research recently. Most of the proposals, however, typically centered around the Euclidean distance and its derivatives. We examine the problem of multimodal similarity search in which users can choose the best one from multiple similarity models for their needs.
Byoung-Kee Yi, Christos Faloutsos
openaire +1 more source
Capped Lp-Norm Graph Embedding for Photo Clustering
Proceedings of the 24th ACM international conference on Multimedia, 2016Photos 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 +2 more
openaire +1 more source
Lp-Norm IDF for Large Scale Image Search
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013The 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 0001 +3 more
openaire +1 more source
Training Lp norm multiple kernel learning in the primal
Neural Networks, 2013Some 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.
Zhizheng Liang +3 more
openaire +3 more sources
Lp Norm Iterative Sparse Solution for EEG Source Localization
IEEE Transactions on Biomedical Engineering, 2007How 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 0001 +3 more
openaire +2 more sources