 | Bruno Apolloni - 2007 - 1126 lapas
...investigated problem as well as of the exploited dataset from any projection of the data comes to lie on the first coordinate (called the first principal...greatest variance on the second coordinate, and so on. Thus, PCA can be used to retain those characteristics of the dataset that contribute most to its variance,... | |
 | Matthew A. Jenks, Paul M. Hasegawa, Shri Mohan Jain - 2007 - 817 lapas
...a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal...greatest variance on the second coordinate, and so on, resulting in dimensionality reduction in a dataset while retaining those characteristics of the dataset... | |
 | Derong Liu, Shumin Fei, Zeng-Guang Hou, Huaguang Zhang, Changyin Sun - 2007 - 1316 lapas
...a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal...greatest variance on the second coordinate, and so on. In this paper, comparative studies on regularization and dimension reduction approaches are given with... | |
 | Markus C. Hemmer - 2007 - 416 lapas
...The new coordinate system may, for instance, show the greatest variance on the first coordinate (ie, first principal component), the second greatest variance on the second coordinate, and so on. The graphical representation of this coordinate system then shows relationships between data points... | |
 | Igor Kononenko, Matjaz Kukar - 2007 - 484 lapas
...data such that the greatest variance by any projection of the data set lies on the first axis (then called the first principal component), the second greatest variance on the second axis, and so on. PCA can be used for reducing dimensionality in a dataset while retaining those characteristics... | |
 | René Pellissier - 2008 - 116 lapas
...a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal...while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring... | |
 | David Aldridge, Gudrun Aldridge - 2008 - 344 lapas
...a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal...greatest variance on the second coordinate, and so on; in this case, the construct 'rhythmically related/melodically related' playing and the construct 'clear/vague'.... | |
 | Orjan G. Martinsen, Sverre Grimnes - 2011 - 488 lapas
...a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal...greatest variance on the second coordinate and so on. In Fig. 8.29 the samples are plotted as circles according to their measured impedance values in the... | |
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PCA is an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest... 