Hakan Erdogan | Sabanci University (original) (raw)

Papers by Hakan Erdogan

2015 23rd European Signal Processing Conference (EUSIPCO), 2015

We present a framework for designing fast and monotonic algorithms for transmission tomography pe... more We present a framework for designing fast and monotonic algorithms for transmission tomography penalized- likelihood image reconstruction. The new algorithms are based on paraboloidal surrogate functions for the log likelihood. Due to the form of the log-likelihood function it is possible to find low curvature surrogate functions that guarantee monotonicity. Unlike previous methods, the proposed surrogate functions lead to monotonic

2013 21st Signal Processing and Communications Applications Conference (SIU), 2013

ABSTRACT Forward-backward pursuit (FBP) is an iterative two stage thresholding method (TST) for s... more ABSTRACT Forward-backward pursuit (FBP) is an iterative two stage thresholding method (TST) for sparse signal recovery. Due to the selection of more indices during the forward step than the ones pruned by the backward step, FBP iteratively enlarges the support estimate. With this structure, FBP does not necessitate the sparsity level to be known a priori in contrast to other TST algorithms such as subspace pursuit (SP) or compressive sampling matching pursuit. In this work, we address optimal selection of forward and backward step sizes for FBP. We analyse the empirical recovery performance of FBP with different step sizes via phase transitions. Moreover, we compare phase transitions of FBP with those of basis pursuit, SP and orthogonal matching pursuit.

... Burada, e˘ger y = z ise, δ(y, z)=1 'dir, di˘ger durumlarda sıfırdır. Ay ve by ise, A mat... more ... Burada, e˘ger y = z ise, δ(y, z)=1 'dir, di˘ger durumlarda sıfırdır. Ay ve by ise, A matrisini ve b vektörünü N satırlı matris ve vektörler olarak parçaladı˘gımızda y'ninci alt matris ve vektöre denk gelir: wk = Ak ˜w + bk. ... [3] David H. Wolpert, “Stacked generalization,” Neural Netw., vol. ...

B-uc aflhc inadequste prrfonnanw of speech recognition systems, an accurate confidence scoring me... more B-uc aflhc inadequste prrfonnanw of speech recognition systems, an accurate confidence scoring mechanism should be employed to un- dentand the user requests correctly. To determine a confidence score fora hypothesis, cemin confidence features are combined. In this work the performance offiller-model based confidence features ham bccn in- vertigtted. Five types of filler model networks were defined: mphonc- netwark phone-network, phane-elass

Straightforward combination of tree search with matching pursuits, which was suggested in 2001 by... more Straightforward combination of tree search with matching pursuits, which was suggested in 2001 by Cotter and Rao, and then later developed by some other authors, has been revisited recently as multipath matching pursuit (MMP). In this comment, we would like to point out some major issues regarding this publication. First, the idea behind MMP is not novel, and the related literature has not been properly referenced. MMP has not been compared to closely related algorithms such as A* orthogonal matching pursuit (A*OMP). The theoretical analyses do ignore the pruning strategies applied by the authors in practice. All these issues have the potential to mislead the reader and lead to misinterpretation of the results. With this short paper, we intend to clarify the relation of MMP to existing literature in the area and compare its performance with A*OMP.

Heuristic search has recently been utilized for compressed sensing signal recovery problem by the... more Heuristic search has recently been utilized for compressed sensing signal recovery problem by the A* Orthogonal Matching Pursuit (A*OMP) algorithm. A*OMP employs A* search on a tree with an OMP-based evaluation of the branches, where the search is terminated when the desired path length is achieved. The algorithm employs effective pruning techniques and cost models which make the tree search practical. Here, we propose two important extensions of A*OMP: We first introduce a novel dynamic cost model that reduces the search time. Second, we modify the termination criterion by stopping the search when ℓ2 norm of the residue is small enough. Following the restricted isometry property, this termination criterion is more appropriate for our purposes. We demonstrate the improvements in terms of both reconstruction accuracy and computation times via a wide range of simulations.

Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, 2004., 2004

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2001

Proceedings of the second international conference on Human Language Technology Research -, 2002

EURASIP Journal on Advances in Signal Processing, 2015

Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transfor... more Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transform (MLLT) applied to spliced static MFCC features yields important performance gains as compared to MFCC+dynamic features in most speech recognition tasks. It is reasonable to regularize LDA transform computation for stability. In this paper, we regularize LDA and heteroschedastic LDA transforms using two methods: (1) Statistical priors for

2015 23rd European Signal Processing Conference (EUSIPCO), 2015

We present a framework for designing fast and monotonic algorithms for transmission tomography pe... more We present a framework for designing fast and monotonic algorithms for transmission tomography penalized- likelihood image reconstruction. The new algorithms are based on paraboloidal surrogate functions for the log likelihood. Due to the form of the log-likelihood function it is possible to find low curvature surrogate functions that guarantee monotonicity. Unlike previous methods, the proposed surrogate functions lead to monotonic

2013 21st Signal Processing and Communications Applications Conference (SIU), 2013

ABSTRACT Forward-backward pursuit (FBP) is an iterative two stage thresholding method (TST) for s... more ABSTRACT Forward-backward pursuit (FBP) is an iterative two stage thresholding method (TST) for sparse signal recovery. Due to the selection of more indices during the forward step than the ones pruned by the backward step, FBP iteratively enlarges the support estimate. With this structure, FBP does not necessitate the sparsity level to be known a priori in contrast to other TST algorithms such as subspace pursuit (SP) or compressive sampling matching pursuit. In this work, we address optimal selection of forward and backward step sizes for FBP. We analyse the empirical recovery performance of FBP with different step sizes via phase transitions. Moreover, we compare phase transitions of FBP with those of basis pursuit, SP and orthogonal matching pursuit.

... Burada, e˘ger y = z ise, δ(y, z)=1 'dir, di˘ger durumlarda sıfırdır. Ay ve by ise, A mat... more ... Burada, e˘ger y = z ise, δ(y, z)=1 'dir, di˘ger durumlarda sıfırdır. Ay ve by ise, A matrisini ve b vektörünü N satırlı matris ve vektörler olarak parçaladı˘gımızda y'ninci alt matris ve vektöre denk gelir: wk = Ak ˜w + bk. ... [3] David H. Wolpert, “Stacked generalization,” Neural Netw., vol. ...

B-uc aflhc inadequste prrfonnanw of speech recognition systems, an accurate confidence scoring me... more B-uc aflhc inadequste prrfonnanw of speech recognition systems, an accurate confidence scoring mechanism should be employed to un- dentand the user requests correctly. To determine a confidence score fora hypothesis, cemin confidence features are combined. In this work the performance offiller-model based confidence features ham bccn in- vertigtted. Five types of filler model networks were defined: mphonc- netwark phone-network, phane-elass

Straightforward combination of tree search with matching pursuits, which was suggested in 2001 by... more Straightforward combination of tree search with matching pursuits, which was suggested in 2001 by Cotter and Rao, and then later developed by some other authors, has been revisited recently as multipath matching pursuit (MMP). In this comment, we would like to point out some major issues regarding this publication. First, the idea behind MMP is not novel, and the related literature has not been properly referenced. MMP has not been compared to closely related algorithms such as A* orthogonal matching pursuit (A*OMP). The theoretical analyses do ignore the pruning strategies applied by the authors in practice. All these issues have the potential to mislead the reader and lead to misinterpretation of the results. With this short paper, we intend to clarify the relation of MMP to existing literature in the area and compare its performance with A*OMP.

Heuristic search has recently been utilized for compressed sensing signal recovery problem by the... more Heuristic search has recently been utilized for compressed sensing signal recovery problem by the A* Orthogonal Matching Pursuit (A*OMP) algorithm. A*OMP employs A* search on a tree with an OMP-based evaluation of the branches, where the search is terminated when the desired path length is achieved. The algorithm employs effective pruning techniques and cost models which make the tree search practical. Here, we propose two important extensions of A*OMP: We first introduce a novel dynamic cost model that reduces the search time. Second, we modify the termination criterion by stopping the search when ℓ2 norm of the residue is small enough. Following the restricted isometry property, this termination criterion is more appropriate for our purposes. We demonstrate the improvements in terms of both reconstruction accuracy and computation times via a wide range of simulations.

Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, 2004., 2004

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2001

Proceedings of the second international conference on Human Language Technology Research -, 2002

EURASIP Journal on Advances in Signal Processing, 2015

Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transfor... more Linear Discriminant Analysis (LDA) followed by a diagonalizing maximum likelihood linear transform (MLLT) applied to spliced static MFCC features yields important performance gains as compared to MFCC+dynamic features in most speech recognition tasks. It is reasonable to regularize LDA transform computation for stability. In this paper, we regularize LDA and heteroschedastic LDA transforms using two methods: (1) Statistical priors for