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2012 IEEE International Workshop on Machine Learning for Signal Processing最新文献

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Link prediction in weighted networks 加权网络中的链路预测
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349745
D. Wind, Morten Mørup
{"title":"Link prediction in weighted networks","authors":"D. Wind, Morten Mørup","doi":"10.1109/MLSP.2012.6349745","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349745","url":null,"abstract":"Many complex networks feature relations with weight information. Some models utilize this information while other ignore the weight information when inferring the structure. In this paper we investigate if edge-weights when modeling real networks, carry important information about the network structure. We compare five prominent models by their ability to predict links both in the presence and absence of weight information. In addition we quantify the models ability to account for the edge-weight information. We find that the complex models generally outperform simpler models when the task is to infer presence of edges, but that simpler models are better at inferring the actual weights.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130306916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
Quantifying spatiotemporal dynamics of twitter replies to news feeds 量化twitter对新闻feed回复的时空动态
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349806
F. Biessmann, Jens-Michalis Papaioannou, A. Harth, M. Jugel, K. Müller, M. Braun
{"title":"Quantifying spatiotemporal dynamics of twitter replies to news feeds","authors":"F. Biessmann, Jens-Michalis Papaioannou, A. Harth, M. Jugel, K. Müller, M. Braun","doi":"10.1109/MLSP.2012.6349806","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349806","url":null,"abstract":"Social network analysis can be used to assess the impact of information published on the web. The spatiotemporal impact of a certain web source on a social network can be of particular interest. We contribute a novel statistical learning algorithm for spatiotemporal impact analysis. To demonstrate our approach we analyze Twitter replies to individual news article along with their geospatial and temporal information. We then compute the multivariate spatiotemporal response pattern of all Twitter replies to information published on a given web source. This quantitative result can be interpreted with respect to a) how much impact a certain web source has on the Twitter-sphere b) where and c) when it reaches it maximal impact. We also show that the proposed approach predicts the dynamics of the social network activity better than classical trend detection methods.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130020046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Transient analysis of convexly constrained mixture methods 凸约束混合方法的瞬态分析
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349801
Mehmet A. Donmez, Huseyin Ozkan, S. Kozat
{"title":"Transient analysis of convexly constrained mixture methods","authors":"Mehmet A. Donmez, Huseyin Ozkan, S. Kozat","doi":"10.1109/MLSP.2012.6349801","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349801","url":null,"abstract":"We study the transient performances of three convexly constrained adaptive combination methods that combine outputs of two adaptive filters running in parallel to model a desired unknown system. We propose a theoretical model for the mean and mean-square convergence behaviors of each algorithm. Specifically, we provide expressions for the time evolution of the mean and the variance of the combination parameters, as well as for the mean square errors. The accuracy of the theoretical models are illustrated through simulations in the case of a mixture of two LMS filters with different step sizes.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124926736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Neural spike detection and localisation via Volterra filtering 通过Volterra滤波的神经脉冲检测和定位
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349733
M. Mboup
{"title":"Neural spike detection and localisation via Volterra filtering","authors":"M. Mboup","doi":"10.1109/MLSP.2012.6349733","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349733","url":null,"abstract":"The spike detection problem is cast into a delay estimation. Using elementary operational calculus, we obtain an explicit characterization of the spike locations, in terms of short time window iterated integrals of the noisy signal. From this characterization, we derive a joint spike detection and localization system where the decision function is implemented as the output of a digital Volterra filter. Simulation results using experimental data shows that the method compares favorably with one of the most successful one in the literature.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125718252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Iterative collaborative filtering for recommender systems with sparse data 稀疏数据推荐系统的迭代协同过滤
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349711
Zhuo Zhang, P. Cuff, S. Kulkarni
{"title":"Iterative collaborative filtering for recommender systems with sparse data","authors":"Zhuo Zhang, P. Cuff, S. Kulkarni","doi":"10.1109/MLSP.2012.6349711","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349711","url":null,"abstract":"Collaborative filtering (CF) is one of the most successful techniques in recommender systems. By utilizing co-rated items of pairwise users for similarity measurements, traditional CF uses a weighted summation to predict unknown ratings based on the available ones. However, in practice, the rating matrix is too sparse to find sufficiently many co-rated items, thus leading to inaccurate predictions. To address the case of sparse data, we propose an iterative CF that updates the similarity and rating matrix. The improved CF incrementally selects reliable subsets of missing ratings based on an adaptive parameter and therefore produces a more credible prediction based on similarity. Experimental results on the MovieLens dataset show that our algorithm significantly outperforms traditional CF, Default Voting, and SVD when the data is 1% sparse. The results also show that in the dense data case our algorithm performs as well as state of art methods.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"7 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129828699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Breast ultrasound images gland segmentation 乳腺超声图像腺体分割
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349748
Rui Braz, António M. G. Pinheiro, J. Moutinho, M. Freire, Manuela Pereira
{"title":"Breast ultrasound images gland segmentation","authors":"Rui Braz, António M. G. Pinheiro, J. Moutinho, M. Freire, Manuela Pereira","doi":"10.1109/MLSP.2012.6349748","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349748","url":null,"abstract":"This paper introduces a study for the segmentation of the breast ultrasound images. The objective is to separate the breast gland, which is the region of interest for the breast cancer diagnosis, from other tissues. Images are pre-processed with four different algorithms that consider the image surrounding: speckle reducing anisotropic diffusion, homomorphic filter, Perona and Malik non-linear diffusion and Moran index. For each image pixel a four bins descriptor is created composed by the corresponding pixels of each of these preprocessed images.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122447680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Optimal portfolios under transaction costs in discrete time markets 离散时间市场中交易成本下的最优投资组合
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349773
Mehmet A. Donmez, Sait Tunç, S. Kozat
{"title":"Optimal portfolios under transaction costs in discrete time markets","authors":"Mehmet A. Donmez, Sait Tunç, S. Kozat","doi":"10.1109/MLSP.2012.6349773","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349773","url":null,"abstract":"We study portfolio investment problem from a probabilistic modeling perspective and study how an investor should distribute wealth over two assets in order to maximize the cumulative wealth. We construct portfolios that provide the optimal growth in i.i.d. discrete time two-asset markets under proportional transaction costs. As the market model, we consider arbitrary discrete distributions on the price relative vectors. To achieve optimal growth, we use threshold portfolios. We demonstrate that under the threshold rebalancing framework, the achievable set of portfolios elegantly form an irreducible Markov chain under mild technical conditions. We evaluate the corresponding stationary distribution of this Markov chain, which provides a natural and efficient method to calculate the cumulative expected wealth. Subsequently, the corresponding parameters are optimized using a brute force approach yielding the growth optimal portfolio under proportional transaction costs in i.i.d. discrete-time two-asset markets.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123047328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Kernelizing Geweke's measures of granger causality 对Geweke的格兰杰因果关系测度进行核化
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349710
P. Amblard, Rémy Vincent, O. Michel, C. Richard
{"title":"Kernelizing Geweke's measures of granger causality","authors":"P. Amblard, Rémy Vincent, O. Michel, C. Richard","doi":"10.1109/MLSP.2012.6349710","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349710","url":null,"abstract":"In this paper we extend Geweke's approach of Granger causality by deriving a nonlinear framework based on functional regression in reproducing kernel Hilbert spaces (RKHS). After giving the definitions of dynamical and instantaneous causality in the Granger sense, we review Geweke's measures. These measures quantify improvement in predicting a time series when the past of another one is taken into account. Geweke's measures are based on linear prediction, and we present an alternative using nonlinear prediction implemented using regularized regression in RKHS. We develop the approach and describe the cross-validation step implemented to optimize the hyperparameters (kernel and regularization parameters). We illustrate the approach on two examples. The first one shows the importance of taking into account side information and possible nonlinear effects. The second one is an illustration of the complete inference problem: surrogate data are generated to create the null hypothesis and the nonlinear measures of causal influence are presented in a test framework.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122037714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Prediction of respiratory motion using wavelet based support vector regression 基于小波支持向量回归的呼吸运动预测
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349742
R. Dürichen, T. Wissel, A. Schweikard
{"title":"Prediction of respiratory motion using wavelet based support vector regression","authors":"R. Dürichen, T. Wissel, A. Schweikard","doi":"10.1109/MLSP.2012.6349742","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349742","url":null,"abstract":"In order to successfully ablate moving tumors in robotic radiosurgery, it is necessary to compensate the motion of inner organs caused by respiration. This can be achieved by tracking the body surface and correlating the external movement with the tumor position as it is implemented in CyberKnife® Synchrony. Due to time delays, errors occur which can be reduced by time series prediction. A new prediction algorithm is presented, which combines á trous wavelet decomposition and support vector regression (wSVR). The algorithm was tested and optimized by grid search on simulated as well as on real patient data set. For these real data, wSVR outperformed a wavelet based least mean square (wLMS) algorithm by >; 13% and standard Support Vector regression (SVR) by >; 7:5%. Using approximate estimates for the optimal parameters wSVR was evaluated on a data set of 20 patients. The overall results suggest that the new approach combines beneficial characteristics in a promising way for accurate motion prediction.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122040978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Improving sparse echo cancellation via convex combination of two NLMS filters with different lengths 利用两个不同长度NLMS滤波器的凸组合改进稀疏回波抵消
2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349774
Álvaro Gonzalo-Ayuso, Magno T. M. Silva, V. Nascimento, J. Arenas-García
{"title":"Improving sparse echo cancellation via convex combination of two NLMS filters with different lengths","authors":"Álvaro Gonzalo-Ayuso, Magno T. M. Silva, V. Nascimento, J. Arenas-García","doi":"10.1109/MLSP.2012.6349774","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349774","url":null,"abstract":"In this paper, we propose a scheme for sparse echo cancellation which uses a convex combination of two normalized least-mean-squares (NLMS) filters with different lengths. As is normally the case in acoustic echo cancellation, the first filter includes a large number of taps to guarantee that the active (i.e., non-null) coefficients of the true echo path are correctly identified. The second filter is a shorter and faster one, intended to span just the region of active coefficients. To identify this active region, we present a method based on clustering of the combined filter coefficients. We also propose two different combination strategies that simultaneously improve steady-state and convergence performance. When the echo path is very sparse, the computational cost incurred by our schemes is just slightly higher than that of a single NLMS filter. Simulation results show the superior performance of the proposed schemes when compared to other methods in the literature.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125554672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
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