Principal Component Analysis - Revision history

Kinoshita at 02:10, 8 August 2014

2014-08-08T02:10:50Z

Kinoshita at 09:30, 4 August 2014

2014-08-04T09:30:38Z

Kinoshita at 09:20, 4 August 2014

2014-08-04T09:20:07Z

Kinoshita at 09:19, 4 August 2014

2014-08-04T09:19:30Z

Show changes

Kinoshita at 08:06, 4 August 2014

2014-08-04T08:06:04Z

Kinoshita at 08:00, 4 August 2014

2014-08-04T08:00:49Z

Kinoshita: Created page with "'''Principal Component Analysis''' is the method that calculates the set of axis whose vector distribution is larger (main axis) from set of vector whose consist of multivaria..."

2014-07-31T01:32:11Z

Created page with "'''Principal Component Analysis''' is the method that calculates the set of axis whose vector distribution is larger (main axis) from set of vector whose consist of multivaria..."

New page

'''Principal Component Analysis''' is the method that calculates the set of axis whose vector distribution is larger (main axis) from set of vector whose consist of multivariates to Multivariate Axis (Multivariate Space).

[[File:Fig-PCA.png]]

@@ Line 1: / Line 1: @@
-'''Principal Component Analysis''' is the method that calculates from vector data set which has elements of Multivariate to the axis (main axis) which has maximum variance when each vector data is projected into that axis on Multivariate Space, and calculates the axis sequentially that is orthogonal(no correlation) to it and has largest variance.
+'''Principal Component Analysis''' is the method that calculates from vector data set which has elements of Multivariate to the axis (principal axis) which has maximum variance when each vector data is projected into that axis on Multivariate Space, and calculates the axis sequentially that is orthogonal(no correlation) to it and has largest variance.
 [[File:Fig-PCA.png]]
@@ Line 13: / Line 13: @@
 <br>
-<div>First, calculates main axis by using [[mrcImagePCA]].</div>
+<div>First, calculates principal axis by using [[mrcImagePCA]].</div>
 <br>

@@ Line 118: / Line 118: @@
 <br>
-<div>[[:File:Output-PCA.tsv.zip|Data that collected up to 10th components from each file]]</div>
+<div>[[:Media:Output-PCA.tsv.zip|Data that collected up to 10th components from each file]]</div>
 <pre>
 	-1002.110000	1962.390000	2375.080000	3780.900000	1531.830000	-3511.960000	-524.329000	1190.540000	-1106.170000	337.342000

@@ Line 180: / Line 180: @@
 <br>
-<div>classify the images based on the scatter plot. If its pattern is above 10, it means that on 3D they can be almost classified.</div>
+<div>Classify the images based on the scatter plot. If its pattern is above 10, it means that on 3D they can be almost classified.</div>
 <br>

@@ Line 1: / Line 1: @@
-'''Principal Component Analysis''' is the method that calculates from vector data set which has elements of Multivariate to the axis (main axis) which has maximum variance when each vector data is projected into that axis on Multivariate Space, and
+'''Principal Component Analysis''' is the method that calculates from vector data set which has elements of Multivariate to the axis (main axis) which has maximum variance when each vector data is projected into that axis on Multivariate Space, and calculates the axis sequentially that is orthogonal(no correlation) to it and has largest variance.
-whose vector distribution is larger (main axis) from set of vector whose consist of multivariates to Multivariate Axis (Multivariate Space).
 [[File:Fig-PCA.png]]

← Older revision		Revision as of 08:00, 4 August 2014
Line 1:		Line 1:
−	'''Principal Component Analysis''' is the method that calculates ~~the~~ set of axis whose vector distribution is larger (main axis) from set of vector whose consist of multivariates to Multivariate Axis (Multivariate Space).	+	'''Principal Component Analysis''' is the method that calculates from vector data set which has elements of Multivariate to the axis (main axis) which has maximum variance when each vector data is projected into that axis on Multivariate Space, and
		+	whose vector distribution is larger (main axis) from set of vector whose consist of multivariates to Multivariate Axis (Multivariate Space).

	[[File:Fig-PCA.png]]		[[File:Fig-PCA.png]]