TY - CHAP
T1 - Dimensionality Reduction
AU - Marri, Madhuri R.
AU - Ramachandran, Lakshmi
AU - Murukannaiah, Pradeep
AU - Ravindra, Padmashree
AU - Paul, Amrita
AU - Lee, Da Young
AU - Funk, David
AU - Murugappan, Shanmugapriya
AU - Hendrix, William
N1 - Publisher Copyright:
© 2014 by Taylor and Francis Group, LLC.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - both curse [8]-a blessing because it implies a lot of information, and a curse because the data might contain a lot of irrelevant information and be tougher to analyze. High-dimensional data is known to pose a wide variety of challenges in the field of statistics. Dimensionality Reduction, or DR, is therefore employed to deal with the issue of high-dimensional data. Specifically, DR is primarily applied when there is a need to: 1. Select only relevant features from the given data (feature selection) or 2. Extract lower-dimensional data from the current form of the data because the current form is too difficult to analyze (feature extraction).
AB - both curse [8]-a blessing because it implies a lot of information, and a curse because the data might contain a lot of irrelevant information and be tougher to analyze. High-dimensional data is known to pose a wide variety of challenges in the field of statistics. Dimensionality Reduction, or DR, is therefore employed to deal with the issue of high-dimensional data. Specifically, DR is primarily applied when there is a need to: 1. Select only relevant features from the given data (feature selection) or 2. Extract lower-dimensional data from the current form of the data because the current form is too difficult to analyze (feature extraction).
UR - https://www.scopus.com/pages/publications/85137475407
UR - https://www.scopus.com/pages/publications/85137475407#tab=citedBy
U2 - 10.1201/b15352-14
DO - 10.1201/b15352-14
M3 - Chapter
AN - SCOPUS:85137475407
SN - 9781439860847
SP - 263
EP - 310
BT - Practical Graph Mining with R
ER -