Results 291 to 300 of about 502,069 (324)

Non-negative Matrix Factorization with Sparseness Constraints

Journal of machine learning research, 2004
Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based ...
P. Hoyer
semanticscholar   +1 more source

Sparse Tomography

SIAM Journal on Scientific Computing, 2013
A wavelet-based sparsity-promoting reconstruction method is studied in the context of tomography with severely limited projection data. Such imaging problems are ill-posed inverse problems, or very sensitive to measurement and modeling errors. The reconstruction method is based on minimizing a sum of a data discrepancy term based on an $\ell^2$-norm ...
Keijo Hämäläinen, Aki Kallonen, Ville Kolehmainen, Matti Lassas, Kati Niinimäki, Samuli Siltanen   +5 more
openaire   +1 more source

Sparseness reduction in collaborative filtering using a nearest neighbour artificial immune system with genetic algorithms

Expert systems with applications, 2019
In collaborative filtering, one of the main challenges that researchers face is sparseness in the data, which is caused by users rating fewer items as the number of items increase in the dataset.
Mlungisi Duma, Bhekisipho Twala
semanticscholar   +1 more source

SC2Net: Sparse LSTMs for Sparse Coding

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
The iterative hard-thresholding algorithm (ISTA) is one of the most popular optimization solvers to achieve sparse codes. However, ISTA suffers from following problems: 1) ISTA employs non-adaptive updating strategy to learn the parameters on each dimension with a fixed learning rate. Such a strategy may lead to inferior performance due
Joey Tianyi Zhou, Kai Di, Jiawei Du, Xi Peng 0001, Hao Yang 0033, Sinno Jialin Pan, Ivor W. Tsang, Yong Liu 0026, Zheng Qin 0004, Rick Siow Mong Goh   +9 more
openaire   +1 more source

FPGA-Based Training Accelerator Utilizing Sparseness of Convolutional Neural Network

International Conference on Field-Programmable Logic and Applications, 2019
Training of convolutional neural networks (CNNs) is almost exclusively performed on large clusters of GPUs. However, it consumes vast amounts of power. Thus, high-speed training systems superior in low-power consumption are desired.
Hiroki Nakahara, Youki Sada, Masayuki Shimoda, Kouki Sayama, Akira Jinguji, Shimpei Sato   +5 more
semanticscholar   +1 more source

Distributed Spanner Construction With Physical Interference: Constant Stretch and Linear Sparseness

IEEE/ACM Transactions on Networking, 2017
This paper presents the first distributed algorithm to construct a spanner for arbitrary ad hoc networks under the physical signal-to-interference-and-noise-ratio (SINR) interference model. Spanner construction is one of the most important techniques for
Dong-Xiao Yu, Li Ning, Yi-Fei Zou, Ji-Guo Yu, Xiu-Zhen Cheng, F. Lau   +5 more
semanticscholar   +1 more source

Highly Sparse Representations From Dictionaries Are Unique and Independent of the Sparseness Measure

Applied and Computational Harmonic Analysis, 2007
This paper appeared as technical report in 2003, see http://hal.inria.fr/inria-00564038/International audienceThe purpose of this paper is to study sparse representations of signals from a general dictionary in a Banach space.
Morten Nielsen
exaly   +2 more sources

Sparse Topical Coding with Sparse Groups

2016
Learning a latent semantic representing from a large number of short text corpora makes a profound practical significance in research and engineering. However, it is difficult to use standard topic models in microblogging environments since microblogs have short length, large amount, snarled noise and irregular modality characters, which prevent topic ...
Min Peng 0002, Qianqian Xie, Jiajia Huang, Jiahui Zhu, Shuang Ouyang, Jimin Huang, Gang Tian   +6 more
openaire   +1 more source

Sparse Coding in Sparse Winner Networks

2007
This paper investigates a mechanism for reliable generation of sparse code in a sparsely connected, hierarchical, learning memory. Activity reduction is accomplished with local competitions that suppress activities of unselected neurons so that costly global competition is avoided.
Janusz A. Starzyk, Yinyin Liu, David D. Vogel   +2 more
openaire   +2 more sources

Sparse boosting

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost.
Zhen James Xiang, Peter J. Ramadge
openaire   +1 more source