(New page: Category:ECE662Spring2014Boutin Category:ECE Category:ECE662 Category:pattern recognition Category:discussion == Discussion about Data for homework 1== [[2014_Spring_...) |
|||
Line 11: | Line 11: | ||
*Question from a student email:"In the Internet I find a lot if data sets, but all of them have n-dim feature vectors. It's very hard to find a feature vector with only one variable. It there a way that we can convey the n-dim feature vector to 1-d feature? If in common, not, do you have any suggestions on where I could find good 1-d feature data?" | *Question from a student email:"In the Internet I find a lot if data sets, but all of them have n-dim feature vectors. It's very hard to find a feature vector with only one variable. It there a way that we can convey the n-dim feature vector to 1-d feature? If in common, not, do you have any suggestions on where I could find good 1-d feature data?" | ||
**You can simply use the first dimension of the feature vector as a one-dimensional feature. You could also use the second dimension, or the third dimension, etc. -pm | **You can simply use the first dimension of the feature vector as a one-dimensional feature. You could also use the second dimension, or the third dimension, etc. -pm | ||
+ | * This is Varun Vasudevan (Email: vasudeva@purdue.edu). I am unsure if I will be able to work on this data set. But if you are willing to share and explain your experiments on the data set, then it would be very helpful to me. Thank You. | ||
---- | ---- | ||
[[2014_Spring_ECE_662_Boutin|Back to main ECE662 Spring 2014 course wiki]] | [[2014_Spring_ECE_662_Boutin|Back to main ECE662 Spring 2014 course wiki]] |
Revision as of 02:36, 11 March 2014
Discussion about Data for homework 1
- Question from a student email:"In the Internet I find a lot if data sets, but all of them have n-dim feature vectors. It's very hard to find a feature vector with only one variable. It there a way that we can convey the n-dim feature vector to 1-d feature? If in common, not, do you have any suggestions on where I could find good 1-d feature data?"
- You can simply use the first dimension of the feature vector as a one-dimensional feature. You could also use the second dimension, or the third dimension, etc. -pm
- This is Varun Vasudevan (Email: vasudeva@purdue.edu). I am unsure if I will be able to work on this data set. But if you are willing to share and explain your experiments on the data set, then it would be very helpful to me. Thank You.