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| 2. |
- Adalbjörnsson, Stefan, et al.
(författare)
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Conjugate priors for Gaussian emission plsa recommender systems
- 2016
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Ingår i: 2016 24th European Signal Processing Conference, EUSIPCO 2016. - 9780992862657 ; 2016-November, s. 2096-2100
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Konferensbidrag (refereegranskat)abstract
- Collaborative filtering for recommender systems seeks to learn and predict user preferences for a collection of items by identifying similarities between users on the basis of their past interest or interaction with the items in question. In this work, we present a conjugate prior regularized extension of Hofmann's Gaussian emission probabilistic latent semantic analysis model, able to overcome the over-fitting problem restricting the performance of the earlier formulation. Furthermore, in experiments using the EachMovie and MovieLens data sets, it is shown that the proposed regularized model achieves significantly improved prediction accuracy of user preferences as compared to the latent semantic analysis model without priors.
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| 3. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
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A Sparse Approach for Estimation of Amplitude Modulated Sinusoids
- 2014
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of spectral analysis of signals composed of sums of multiple amplitude modulated, possibly harmonically related, sinusoids using a sparse approach. By separating the nonlinear frequency variables using a dictionary of possible frequency components as well as a spline basis for the amplitude modulation, results in a convex criterion which can be efficiently solved without worrisome local minima. The resulting method makes no model order assumption and automatically estimates both the signal parameters and their amplitude modulations
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| 4. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
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Efficient Block and Time-Recursive Estimation of Sparse Volterra Systems
- 2012
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Ingår i: 2012 IEEE Statistical Signal Processing Workshop (SSP), Proceedings of. - 9781467301831 ; , s. 173-176
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Konferensbidrag (refereegranskat)abstract
- We investigate the application of non-convex penalized least squares for parameter estimation in the Volterra model. Sparsity is promoted by introducing a weighted !q penalty on the parameters and efficient batch and time recursive algorithms are devised based on the cyclic coordinate descent approach. Numerical examples illustrate the improved performance of the proposed algorithms as compared the weighted !1 norm.
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| 5. |
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| 6. |
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| 7. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
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High resolution sparse estimation of exponentially decaying two-dimensional signals
- 2014
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Ingår i: European Signal Processing Conference. - 2219-5491.
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Konferensbidrag (refereegranskat)abstract
- In this work, we consider the problem of high-resolution estimation of the parameters detailing a two-dimensional (2-D) signal consisting of an unknown number of exponentially decaying sinusoidal components. Interpreting the estimation problem as a block (or group) sparse representation problem allows the decoupling of the 2-D data structure into a sum of outer-products of 1-D damped sinusoidal signals with unknown damping and frequency. The resulting non-zero blocks will represent each of the 1-D damped sinusoids, which may then be used as non-parametric estimates of the corresponding 1-D signals; this implies that the sought 2-D modes may be estimated using a sequence of 1-D optimization problems. The resulting sparse representation problem is solved using an iterative ADMM-based algorithm, after which the damping and frequency parameter can be estimated by a sequence of simple 1-D optimization problems.
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| 9. |
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| 10. |
- Adalbjörnsson, Stefan Ingi, et al.
(författare)
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Sparse Estimation Of Spectroscopic Signals
- 2011
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Ingår i: European Signal Processing Conference. - 2219-5491. ; 2011, s. 333-337
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Konferensbidrag (refereegranskat)abstract
- This work considers the semi-parametric estimation of sparse spec- troscopic signals, aiming to form a detailed spectral representation of both the frequency content and the spectral line widths of the oc- curring signals. Extending on the recent FOCUSS-based SLIM al- gorithm, we propose an alternative prior for a Bayesian formulation of this sparse reconstruction method, exploiting a proposed suitable prior for the noise variance. Examining three common models for spectroscopic signals, the introduced technique allows for reliable estimation of the characteristics of these models. Numerical sim- ulations illustrate the improved performance of the proposed tech- nique.
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