Results 221 to 230 of about 281,586 (259)
Some of the next articles are maybe not open access.
Communications of the ACM, 2010
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pixel (and time). Text documents may be represented in a vector space where each word in the dictionary incurs a dimension.
Nir Ailon, Bernard Chazelle
openaire +1 more source
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pixel (and time). Text documents may be represented in a vector space where each word in the dictionary incurs a dimension.
Nir Ailon, Bernard Chazelle
openaire +1 more source
Communications in Statistics - Theory and Methods, 2016
ABSTRACTL2Boosting is an effective method for constructing model. In the case of high-dimensional setting, Buhlmann and Yu (2003) proposed the componentwise L2Boosting, but componentwise L2Boosting can only fit a special limited model. In this paper, by combining a boosting and sufficient dimension reduction method, e.g., sliced inverse regression (SIR)
Junlong Zhao, Xiuli Zhao
openaire +1 more source
ABSTRACTL2Boosting is an effective method for constructing model. In the case of high-dimensional setting, Buhlmann and Yu (2003) proposed the componentwise L2Boosting, but componentwise L2Boosting can only fit a special limited model. In this paper, by combining a boosting and sufficient dimension reduction method, e.g., sliced inverse regression (SIR)
Junlong Zhao, Xiuli Zhao
openaire +1 more source
WIREs Computational Statistics, 2014
Information in the data often has far fewer degrees of freedom than the number of variables encoding the data. Dimensionality reduction attempts to reduce the number of variables used to describe the data. In this article, we shall survey some dimension reduction techniques that are robust.
Chenouri, Shojaeddin +2 more
openaire +2 more sources
Information in the data often has far fewer degrees of freedom than the number of variables encoding the data. Dimensionality reduction attempts to reduce the number of variables used to describe the data. In this article, we shall survey some dimension reduction techniques that are robust.
Chenouri, Shojaeddin +2 more
openaire +2 more sources
Double Shrinking Sparse Dimension Reduction
IEEE Transactions on Image Processing, 2013Learning tasks such as classification and clustering usually perform better and cost less (time and space) on compressed representations than on the original data. Previous works mainly compress data via dimension reduction. In this paper, we propose "double shrinking" to compress image data on both dimensionality and cardinality via building either ...
Tianyi, Zhou, Dacheng, Tao
openaire +2 more sources
Sufficient Dimension Reduction and Kernel Dimension Reduction
2023Benyamin Ghojogh +3 more
openaire +1 more source
2015
A real system, especially a distributed parameter system, may havehigh or even infinite dimensions of freedom (DOF). When the DOF ofa model is too high, all inversion methods that we have learnedbecome inefficient and the inverse problem becomes unsolvablebecause of data and computational limitations.
Ne-Zheng Sun, Alexander Sun
openaire +1 more source
A real system, especially a distributed parameter system, may havehigh or even infinite dimensions of freedom (DOF). When the DOF ofa model is too high, all inversion methods that we have learnedbecome inefficient and the inverse problem becomes unsolvablebecause of data and computational limitations.
Ne-Zheng Sun, Alexander Sun
openaire +1 more source
Dimension reduction techniques
2016Large datasets, as well as data consisting of a large number of features, present computational problems in the training of predictive models. In this chapter we discuss several useful techniques for reducing the dimension of a given dataset, that is reducing the number of data points or number of features, often employed in order to make predictive ...
openaire +1 more source
Dimension Reduction and Its Applications
2003This chapter is motivated by our attempt to answer pertinent questions concerning a number of real data sets, some of which are listed below.
Cizek, P., Xia, Y.
openaire +2 more sources
Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
exaly