TY  - CONF
ID  - Frank2005
T1  - Mdspolar: A new approach for dimension reduction to visualize high dimensional data
A1  - Rehm, Frank
A1  - Klawonn, Frank
A1  - Kruse, Rudolf
TI  - Advances inIntelligent Data Analysis VI - Proc. of the 6th Int. Symp. on IDA 2005
T3  - Lecture Notes in Computer Science
Y1  - 2005
VL  - 3646
SP  - 316
EP  - 327
PB  - Springer
CY  - Berlin, Heidelberg
SN  - 978-3-540-28795-7
SN  - 0302-9743
M2  - doi: 10.1007/11552253_29
N2  - Many applications in science and business such as signal analysis or costumer segmentation deal with large amounts of data which are usually high dimensional in the feature space. As a part of preprocessing and exploratory data analysis, visualization of the data helps to decide which kind of method probably leads to good results. Since the visual assessment of a feature space that has more than three dimensions is not possible, it becomes necessary to find an appropriate visualization scheme for such datasets. In this paper we present a new approach for dimension reduction to visualize high dimensional data. Our algorithm transforms high dimensional feature vectors into two-dimensional feature vectors under the constraints that the length of each vector is preserved and that the angles between vectors approximate the corresponding angles in the high dimensional space as good as possible, enabling us to come up with an efficient computing scheme.
ER  -