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

Towards dictionary learning from images with non Gaussian noise 基于非高斯噪声图像的字典学习

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349731

P. Chainais

引用次数: 5

Identifying modular relations in complex brain networks 识别复杂大脑网络中的模块关系

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349739

Kasper Winther Andersen, Morten Mørup, H. Siebner, Kristoffer Hougaard Madsen, L. K. Hansen

引用次数: 13

The eighth annual MLSP competition: Overview 第八届年度MLSP竞赛:概述

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349769

Ken Montanez, Weifeng Liu, V. Calhoun, Catherine Huang, K. Hild

引用次数: 10

Online learning for quality-driven unequal protection of scalable video 以质量为导向的在线学习对可扩展视频的不平等保护

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349781

A. Khalek, C. Caramanis, R. Heath

{"title":"Online learning for quality-driven unequal protection of scalable video","authors":"A. Khalek, C. Caramanis, R. Heath","doi":"10.1109/MLSP.2012.6349781","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349781","url":null,"abstract":"Video packet losses affect perceived video quality non-uniformly due to several factors related to video encoding such as inter-frame coding and motion compensation as well as due to psycho-visual perception of natural scenes with unequal motion. This motivates protecting video packets unequally based on their loss visibility. This paper proposes an adaptive online algorithm for unequal error protection driven by two key motivations: On one hand, for real-time video, where a video sequence is not pre-encoded, an offline approach is infeasible and determining the unequal protection levels to maintain a target video quality level must be performed online. On the other hand, an online approach enables adapting to scene changes as well as changes in video temporal and spatial characteristics. The proposed online algorithm uses local linear regression to learn the mapping between packet losses from each scalable video layer and quality degradation without assuming an underlying statistical model. The notion of locality captures the similarity in video scene characteristics as well as proximity in time. The algorithm provably guarantees an average target video quality level and converges rapidly to a stable solution. Furthermore, it provides a bias/variance tradeoff between factual estimation of loss visibility and fine adaptation to the changing video temporal characteristics.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"9 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":"116847385","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

Haussdorff and hellinger for colorimetric sensor array classification

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349724

T. S. Alstrøm, B. S. Jensen, Mikkel N. Schmidt, N. Kostesha, J. Larsen

引用次数: 0

Simultaneous and proportional control of 2D wrist movements with myoelectric signals 用肌电信号同时和比例控制二维手腕运动

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349712

J. Hahne, Hubertus Rehbaum, F. Biessmann, F. Meinecke, K. Müller, N. Jiang, D. Farina, L. Parra

引用次数: 34

Helicopter vibration sensor selection using data visualisation 利用数据可视化选择直升机振动传感器

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349808

W. S. Gill, I. Nabney, D. Wells

{"title":"Helicopter vibration sensor selection using data visualisation","authors":"W. S. Gill, I. Nabney, D. Wells","doi":"10.1109/MLSP.2012.6349808","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349808","url":null,"abstract":"The main objective of the project† is to enhance the already effective health-monitoring system (HUMS) for helicopters by analysing structural vibrations to recognise different flight conditions directly from sensor information. The goal of this paper is to develop a new method to select those sensors and frequency bands that are best for detecting changes in flight conditions. We projected frequency information to a 2-dimensional space in order to visualise flight-condition transitions using the Generative Topographic Mapping (GTM) and a variant which supports simultaneous feature selection. We created an objective measure of the separation between different flight conditions in the visualisation space by calculating the Kullback-Leibler (KL) divergence between Gaussian mixture models (GMMs) fitted to each class: the higher the KL-divergence, the better the interclass separation. To find the optimal combination of sensors, they were considered in pairs, triples and groups of four sensors. The sensor triples provided the best result in terms of KL-divergence. We also found that the use of a variational training algorithm for the GMMs gave more reliable results.","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":"133073670","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: 2

A SIFT-point distribution-based method for head pose estimation 基于sift点分布的头部姿态估计方法

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349751

Nastaran Ghadarghadar, E. Cansizoglu, Peng Zhang, Deniz Erdoğmuş

引用次数: 2

Local linear approximation of principal curve projections 主曲线投影的局部线性逼近

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349764

Peng Zhang, E. Cansizoglu, Deniz Erdoğmuş

{"title":"Local linear approximation of principal curve projections","authors":"Peng Zhang, E. Cansizoglu, Deniz Erdoğmuş","doi":"10.1109/MLSP.2012.6349764","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349764","url":null,"abstract":"In previous work we introduced principal surfaces as hyperridges of probability distributions in a differential geometrical sense. Specifically, given an n-dimensional probability distribution over real-valued random vectors, a point on the d-dimensional principal surface is a local maximizer of the distribution in the subspace orthogonal to the principal surface at that point. For twice continuously differentiable distributions, the surface is characterized by the gradient and the Hessian of the distribution. Furthermore, the nonlinear projections of data points to the principal surface for dimension reduction is ideally given by the solution trajectories of differential equations that are initialized at the data point and whose tangent vectors are determined by the Hessian eigenvectors. In practice, data dimension reduction using numerical integration based differential equation solvers are found to be computationally expensive for most machine learning applications. Consequently, in this paper, we propose a local linear approximation to achieve this dimension reduction without significant loss of accuracy while reducing computational complexity. The proposed method is demonstrated on synthetic datasets.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"9 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":"122017117","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

Nonlinear data description with Principal Polynomial Analysis 基于主多项式分析的非线性数据描述

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349786

Valero Laparra, D. Tuia, S. Jiménez, Gustau Camps-Valls, J. Malo

引用次数: 10