42 search results - page 4 / 9 » Disk-Based Sampling for Outlier Detection in High Dimensiona... |
IV
2007
IEEE
14 years 1 months ago
2007
IEEE
High-dimensional data is, by its nature, difficult to visualise. Many current techniques involve reducing the dimensionality of the data, which results in a loss of information. ...
BMCBI
2010
13 years 7 months ago
2010
Background: Data generated using `omics' technologies are characterized by high dimensionality, where the number of features measured per subject vastly exceeds the number of...
GFKL
2004
Springer
14 years 25 days ago
2004
Springer
Abstract. A method for measuring the density of data sets that contain an unknown number of clusters of unknown sizes is proposed. This method, called Pareto Density Estimation (PD...
NN
2010
Springer
13 years 5 months ago
2010
Springer
The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it can be used for various d...
MICCAI
2005
Springer
14 years 8 months ago
2005
Springer
In this paper, we propose a new approach to detect activated time series in functional MRI using support vector clustering (SVC). We extract Fourier coefficients as the features of...