The Experts below are selected from a list of 306 Experts worldwide ranked by ideXlab platform
Mohsen Kompanyzareh - One of the best experts on this subject based on the ideXlab platform.
-
uninformative variable elimination assisted by gram schmidt Orthogonalization successive projection algorithm for descriptor selection in qsar
Chemometrics and Intelligent Laboratory Systems, 2013Co-Authors: Nematollah Omidikia, Mohsen KompanyzarehAbstract:Abstract Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well as collinearity reliability of the regression coefficient's magnitude is suspicious. Successive Projection Algorithm (SPA) and Gram–Schmidt Orthogonalization (GSO) were implemented as pre-selection technique for removing collinearity and redundancy among variables in the model. Uninformative variable elimination-partial least squares (UVE-PLS) was performed on the pre-selected data set and C value 's were calculated for each descriptor. In this case the C value 's of UVE assisted by SPA or GSO could be used in order to rank the variables according to their importance. Leave-many-out cross-validation (LMO-CV) was applied to ordered descriptors for selecting optimal number of descriptors. Selwood data including 31 molecules and 53 descriptors, and anti-HIV data including 107 molecules and 160 descriptors were utilized in this study. When GSO pre-selection method is used for the Selwood data and SPA for the anti-HIV data set, obtained results were desired not only in the prediction ability of the constructed model but also in the number of selected informative descriptors. By applying GSO-UVE-PLS to the Selwood data, in an optimized condition, seven descriptors out of 53 were selected with q 2 = 0.769 and R 2 = 0.915. Also applying SPA-UVE-PLS on the anti-HIV data, nine descriptors were selected out of 160 with q 2 = 0.81, R 2 = 0.84 and Q 2 F3 = 0.8.
Nematollah Omidikia - One of the best experts on this subject based on the ideXlab platform.
-
uninformative variable elimination assisted by gram schmidt Orthogonalization successive projection algorithm for descriptor selection in qsar
Chemometrics and Intelligent Laboratory Systems, 2013Co-Authors: Nematollah Omidikia, Mohsen KompanyzarehAbstract:Abstract Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well as collinearity reliability of the regression coefficient's magnitude is suspicious. Successive Projection Algorithm (SPA) and Gram–Schmidt Orthogonalization (GSO) were implemented as pre-selection technique for removing collinearity and redundancy among variables in the model. Uninformative variable elimination-partial least squares (UVE-PLS) was performed on the pre-selected data set and C value 's were calculated for each descriptor. In this case the C value 's of UVE assisted by SPA or GSO could be used in order to rank the variables according to their importance. Leave-many-out cross-validation (LMO-CV) was applied to ordered descriptors for selecting optimal number of descriptors. Selwood data including 31 molecules and 53 descriptors, and anti-HIV data including 107 molecules and 160 descriptors were utilized in this study. When GSO pre-selection method is used for the Selwood data and SPA for the anti-HIV data set, obtained results were desired not only in the prediction ability of the constructed model but also in the number of selected informative descriptors. By applying GSO-UVE-PLS to the Selwood data, in an optimized condition, seven descriptors out of 53 were selected with q 2 = 0.769 and R 2 = 0.915. Also applying SPA-UVE-PLS on the anti-HIV data, nine descriptors were selected out of 160 with q 2 = 0.81, R 2 = 0.84 and Q 2 F3 = 0.8.
-
Uninformative variable elimination assisted by Gram–Schmidt Orthogonalization/successive projection algorithm for descriptor selection in QSAR
Chemometrics and Intelligent Laboratory Systems, 2013Co-Authors: Nematollah Omidikia, Mohsen Kompany-zarehAbstract:Abstract Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well as collinearity reliability of the regression coefficient's magnitude is suspicious. Successive Projection Algorithm (SPA) and Gram–Schmidt Orthogonalization (GSO) were implemented as pre-selection technique for removing collinearity and redundancy among variables in the model. Uninformative variable elimination-partial least squares (UVE-PLS) was performed on the pre-selected data set and C value 's were calculated for each descriptor. In this case the C value 's of UVE assisted by SPA or GSO could be used in order to rank the variables according to their importance. Leave-many-out cross-validation (LMO-CV) was applied to ordered descriptors for selecting optimal number of descriptors. Selwood data including 31 molecules and 53 descriptors, and anti-HIV data including 107 molecules and 160 descriptors were utilized in this study. When GSO pre-selection method is used for the Selwood data and SPA for the anti-HIV data set, obtained results were desired not only in the prediction ability of the constructed model but also in the number of selected informative descriptors. By applying GSO-UVE-PLS to the Selwood data, in an optimized condition, seven descriptors out of 53 were selected with q 2 = 0.769 and R 2 = 0.915. Also applying SPA-UVE-PLS on the anti-HIV data, nine descriptors were selected out of 160 with q 2 = 0.81, R 2 = 0.84 and Q 2 F3 = 0.8.
-
Jackknife-Based Selection of Gram−Schmidt Orthogonalized Descriptors in QSAR
Journal of chemical information and modeling, 2010Co-Authors: Mohsen Kompany-zareh, Nematollah OmidikiaAbstract:This study is an implementation of a robust jackknife-based descriptor selection procedure assisted with Gram−Schmidt Orthogonalization. Selwood data including 31 molecules and 53 descriptors was c...
Mohsen Kompany-zareh - One of the best experts on this subject based on the ideXlab platform.
-
Uninformative variable elimination assisted by Gram–Schmidt Orthogonalization/successive projection algorithm for descriptor selection in QSAR
Chemometrics and Intelligent Laboratory Systems, 2013Co-Authors: Nematollah Omidikia, Mohsen Kompany-zarehAbstract:Abstract Employment of Uninformative Variable Elimination (UVE) as a robust variable selection method is reported in this study. Each regression coefficient represents the contribution of the corresponding variable in the established model, but in the presence of uninformative variables as well as collinearity reliability of the regression coefficient's magnitude is suspicious. Successive Projection Algorithm (SPA) and Gram–Schmidt Orthogonalization (GSO) were implemented as pre-selection technique for removing collinearity and redundancy among variables in the model. Uninformative variable elimination-partial least squares (UVE-PLS) was performed on the pre-selected data set and C value 's were calculated for each descriptor. In this case the C value 's of UVE assisted by SPA or GSO could be used in order to rank the variables according to their importance. Leave-many-out cross-validation (LMO-CV) was applied to ordered descriptors for selecting optimal number of descriptors. Selwood data including 31 molecules and 53 descriptors, and anti-HIV data including 107 molecules and 160 descriptors were utilized in this study. When GSO pre-selection method is used for the Selwood data and SPA for the anti-HIV data set, obtained results were desired not only in the prediction ability of the constructed model but also in the number of selected informative descriptors. By applying GSO-UVE-PLS to the Selwood data, in an optimized condition, seven descriptors out of 53 were selected with q 2 = 0.769 and R 2 = 0.915. Also applying SPA-UVE-PLS on the anti-HIV data, nine descriptors were selected out of 160 with q 2 = 0.81, R 2 = 0.84 and Q 2 F3 = 0.8.
-
Jackknife-Based Selection of Gram−Schmidt Orthogonalized Descriptors in QSAR
Journal of chemical information and modeling, 2010Co-Authors: Mohsen Kompany-zareh, Nematollah OmidikiaAbstract:This study is an implementation of a robust jackknife-based descriptor selection procedure assisted with Gram−Schmidt Orthogonalization. Selwood data including 31 molecules and 53 descriptors was c...
Tomoaki Ohtsuki - One of the best experts on this subject based on the ideXlab platform.
-
Orthogonal beamforming using Gram-Schmidt Orthogonalization for multi-user MIMO downlink system
EURASIP Journal on Wireless Communications and Networking, 2011Co-Authors: Kunitaka Matsumura, Tomoaki OhtsukiAbstract:Simultaneous transmission to multiple users using orthogonal beamforming, known as space-division multiple-access (SDMA), is capable of achieving very high throughput in multiple-input multiple-output (MIMO) broadcast channel. In this paper, we propose a new orthogonal beamforming algorithm to achieve high capacity performance in MIMO broadcast channel. In the proposed method, the base station generates a unitary beamforming vector set using Gram-Schmidt Orthogonalization. We extend the algorithm of opportunistic SDMA with limited feedback (LF-OSDMA) to guarantee that the system never loses multiplexing gain for fair comparison with the proposed method by informing unallocated beams. We show that the proposed method can achieve a significantly higher sum capacity than LF-OSDMA and the extended LF-OSDMA without a large increase in the amount of feedback bits and latency.
-
VTC Spring - Orthogonal Beamforming Using Gram-Schmidt Orthogonalization for Multi-User MIMO Downlink System
2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), 2011Co-Authors: Kunitaka Matsumura, Tomoaki OhtsukiAbstract:Simultaneous transmission to multiple users using orthogonal beamforming, known as space-division multiple-access (SDMA), is capable of achieving very high throughput in multiple-input multiple-output (MIMO) broadcast channel. In this paper, we propose a new orthogonal beamforming algorithm to achieve high capacity performance in MIMO broadcast channel. In the proposed algorithm, the base station generates a unitary beamforming vector set using Gram-Schmidt Orthogonalization. We extend the algorithm of LF-OSDMA (Opportunistic SDMA with Limited Feedback) to guarantee that the system never loses multiplexing gain for fair comparison with the proposed algorithm by informing unallocated beams. Finally, we show that the proposed method can achieve a significantly higher sum capacity than LF- OSDMA and the extended LF- OSDMA without a large increase in the amount of feedback bits and latency.
E. D. Davis - One of the best experts on this subject based on the ideXlab platform.
-
Uniform approximation of wave functions with improved semiclassical transformation amplitudes and Gram-Schmidt Orthogonalization
Physical Review A, 2004Co-Authors: E. D. DavisAbstract:Semiclassical transformation theory implies an integral representation for stationary-state wave functions {psi}{sub m}(q) in terms of angle-action variables ({theta},J). It is a particular solution of Schroedinger's time-independent equation when terms of order ({Dirac_h}/2{pi}){sup 2} and higher are omitted, but the preexponential factor A(q,{theta}) in the integrand of this integral representation does not possess the correct dependence on q. The origin of the problem is identified: the standard unitarity condition invoked in semiclassical transformation theory does not fix adequately in A(q,{theta}) a factor which is a function of the action J written in terms of q and {theta}. A prescription for an improved choice of this factor, based on successfully reproducing the leading behavior of wave functions in the vicinity of potential minima, is outlined. Exact evaluation of the modified integral representation via the residue theorem is possible. It yields wave functions which are not, in general, orthogonal. However, closed-form results obtained after Gram-Schmidt Orthogonalization bear a striking resemblance to the exact analytical expressions for the stationary-state wave functions of the various potential models considered (namely, a Poeschl-Teller oscillator and the Morse oscillator)
