Results 211 to 220 of about 1,216,046 (262)
Numerically stable locality-preserving partial least squares discriminant analysis for efficient dimensionality reduction and classification of high-dimensional data. [PDF]
HeliyonAhmad NA.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Forecasting high-dimensional data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010We propose a method for forecasting high-dimensional data (hundreds of attributes, trillions of attribute combinations) for a duration of several months. Our motivating application is guaranteed display advertising, a multi-billion dollar industry, whereby advertisers can buy targeted (high-dimensional) user visits from publishers many months or even ...
Deepak Agarwal +4 more
openaire +2 more sources
Feature selection for high-dimensional data
Computational Management Science, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
DESTRERO A +4 more
openaire +4 more sources
Clustering High-Dimensional Data
2015This chapter introduces the task of clustering, concerning the definition of a structure aggregating the data, and the challenges related to its application to the unsupervised analysis of high-dimensional data. In the recent literature, many approaches have been proposed for facing this problem, as the development of efficient clustering methods for ...
MASULLI, FRANCESCO, ROVETTA, STEFANO
openaire +3 more sources
ACM Transactions on Database Systems
We introduce an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. Our approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickly approximated using statistical or linear programming techniques.
Sachin Basil John +2 more
openaire +3 more sources
We introduce an approach to supporting high-dimensional data cubes at interactive query speeds and moderate storage cost. Our approach is based on binary(-domain) data cubes that are judiciously partially materialized; the missing information can be quickly approximated using statistical or linear programming techniques.
Sachin Basil John +2 more
openaire +3 more sources
A telescope for high-dimensional data
Computing in Science & Engineering, 2006Muscular dystrophy is a degenerative disease that destroys muscles and ultimately kills its victims. Researchers worldwide are racing to find a cure by trying to uncover the genetic processes that cause it. Given that a key process is muscle development, researchers at a consortium of 10 institutions are studying 1,000 men and women, ages 18 to 40 ...
openaire +2 more sources
Clustering high dimensional data
WIREs Data Mining and Knowledge Discovery, 2012AbstractHigh‐dimensional data, i.e., data described by a large number of attributes, pose specific challenges to clustering. The so‐called ‘curse of dimensionality’, coined originally to describe the general increase in complexity of various computational problems as dimensionality increases, is known to render traditional clustering algorithms ...
openaire +1 more source
Dimensionality Reduction for Registration of High-Dimensional Data Sets
IEEE Transactions on Image Processing, 2013Registration of two high-dimensional data sets often involves dimensionality reduction to yield a single-band image from each data set followed by pairwise image registration. We develop a new application-specific algorithm for dimensionality reduction of high-dimensional data sets such that the weighted harmonic mean of Cramér-Rao lower bounds for the
Xu, Min, Chen, Hao, Varshney, Pramod K.
openaire +3 more sources