Abstract
This paper provides, for the first time, exact analytical expressions for the first moment of the true error of linear discriminant analysis (LDA) when the data are univariate and taken from two stochastic Gaussian processes. We assume a general setting in which the sample data from each class do not need to be identically distributed or independent within or between classes. As an application of this framework, we characterize the performance of LDA in situations that the data are generated from autoregressive models of the first order.
| Original language | English |
|---|---|
| Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Pages | 3437-3441 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - Oct 18 2013 |
| Externally published | Yes |
| Event | 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada Duration: May 26 2013 → May 31 2013 |
Other
| Other | 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 5/26/13 → 5/31/13 |
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Keywords
- Expected error
- Gaussian processes
- Linear discriminant analysis
- Non-i.i.d data
- Stochastic settings
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering
Cite this
Performance of linear discriminant analysis in stochastic settings. / Zollanvari, Amin; Hua, Jianping; Dougherty, Edward R.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2013. p. 3437-3441 6638296.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Performance of linear discriminant analysis in stochastic settings
AU - Zollanvari, Amin
AU - Hua, Jianping
AU - Dougherty, Edward R.
PY - 2013/10/18
Y1 - 2013/10/18
N2 - This paper provides, for the first time, exact analytical expressions for the first moment of the true error of linear discriminant analysis (LDA) when the data are univariate and taken from two stochastic Gaussian processes. We assume a general setting in which the sample data from each class do not need to be identically distributed or independent within or between classes. As an application of this framework, we characterize the performance of LDA in situations that the data are generated from autoregressive models of the first order.
AB - This paper provides, for the first time, exact analytical expressions for the first moment of the true error of linear discriminant analysis (LDA) when the data are univariate and taken from two stochastic Gaussian processes. We assume a general setting in which the sample data from each class do not need to be identically distributed or independent within or between classes. As an application of this framework, we characterize the performance of LDA in situations that the data are generated from autoregressive models of the first order.
KW - Expected error
KW - Gaussian processes
KW - Linear discriminant analysis
KW - Non-i.i.d data
KW - Stochastic settings
UR - http://www.scopus.com/inward/record.url?scp=84890456561&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890456561&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638296
DO - 10.1109/ICASSP.2013.6638296
M3 - Conference contribution
SN - 9781479903566
SP - 3437
EP - 3441
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -