2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)最新文献_第3页

Joint learning of deep multi-scale features and diversified metrics for hyperspectral image classification 基于深度多尺度特征和多样化指标的高光谱图像分类联合学习

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168157

Z. Gong, P. Zhong, Yang Yu, Jiaxin Shan, W. Hu

{"title":"Joint learning of deep multi-scale features and diversified metrics for hyperspectral image classification","authors":"Z. Gong, P. Zhong, Yang Yu, Jiaxin Shan, W. Hu","doi":"10.1109/MLSP.2017.8168157","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168157","url":null,"abstract":"Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected. To solve the problem, a multi-scale CNN which can extract multi-scale features is designed for hyperspectral image classification. Furthermore, D-DSML, a diversified metric, is proposed to further improve the representational ability of deep methods. In this paper, a D-DSML-MSCNN method, which jointly learns deep multi-scale features and diversified metrics for hyperspectral image classification, is proposed to take both advantages of D-DSML and MSCNN. Experiments are conducted on Pavia University data to show the effectiveness of our method for hyperspectral image classification. The results show the advantage of our method when compared with other recent results.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"67 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80954721","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

Online function minimization with convex random relu expansions 凸随机relu展开的在线函数最小化

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168109

Laurens Bliek, M. Verhaegen, S. Wahls

引用次数: 5

Estimation of interventional effects of features on prediction 特征对预测介入效应的估计

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168175

Patrick Blöbaum, Shohei Shimizu

引用次数: 5

Rényi entropy based mutual information for semi-supervised bird vocalization segmentation 基于rsamnyi熵的互信息半监督鸟类发声分割

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168118

Anshul Thakur, V. Abrol, Pulkit Sharma, Padmanabhan Rajan

引用次数: 11

Adaptive sparse modeling and shifted-poisson likelihood based approach for low-dosect image reconstruction 基于位移泊松似然的自适应稀疏建模和低剂量图像重建方法

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168124

Siqi Ye, S. Ravishankar, Y. Long, J. Fessler

{"title":"Adaptive sparse modeling and shifted-poisson likelihood based approach for low-dosect image reconstruction","authors":"Siqi Ye, S. Ravishankar, Y. Long, J. Fessler","doi":"10.1109/MLSP.2017.8168124","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168124","url":null,"abstract":"Recent research in computed tomographic imaging has focused on developing techniques that enable reduction of the X-ray radiation dose without loss of quality of the reconstructed images or volumes. While penalized weighted-least squares (PWLS) approaches have been popular for CT image reconstruction, their performance degrades for very low dose levels due to the inaccuracy of the underlying WLS statistical model. We propose a new formulation for low-dose CT image reconstruction based on a shifted-Poisson model based likelihood function and a data-adaptive regularizer using the sparsifying transform model for images. The sparsifying transform is pre-learned from a dataset of patches extracted from CT images. The nonconvex cost function of the proposed penalized-likelihood reconstruction with sparsifying transforms regularizer (PL-ST) is optimized by alternating between a sparse coding step and an image update step. The image update step deploys a series of convex quadratic majorizers that are optimized using a relaxed linearized augmented Lagrangian method with ordered-subsets, reducing the number of (expensive) forward and backward projection operations. Numerical experiments show that for low dose levels, the proposed data-driven PL-ST approach outperforms prior methods employing a nonadaptive edge-preserving regularizer. PL-ST also outperforms prior PWLS-ST approach at very low X-ray doses.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"86 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85993930","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: 5

Compact kernel classifiers trained with minimum classification error criterion 以最小分类误差标准训练的紧凑核分类器

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168184

Ryoma Tani, Hideyuki Watanabe, S. Katagiri, M. Ohsaki

引用次数: 0

DTW-Approach for uncorrelated multivariate time series imputation 非相关多元时间序列imputation的dtw方法

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168165

Thi-Thu-Hong Phan, É. Poisson, A. Bigand, A. Lefebvre

引用次数: 2

Approximate method of variational Bayesian matrix factorization with sparse prior 稀疏先验变分贝叶斯矩阵分解的近似方法

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168156

Ryota Kawasumi, K. Takeda

引用次数: 0

Missing component restoration for masked speech signals based on time-domain spectrogram factorization 基于时域谱图分解的被屏蔽语音信号缺失分量恢复

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168125

Shogo Seki, H. Kameoka, T. Toda, K. Takeda

引用次数: 0

Differential mutual information forward search for multi-kernel discriminant-component selection with an application to privacy-preserving classification 差分互信息前向搜索多核鉴别成分选择及其在隐私保护分类中的应用

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168177

Thee Chanyaswad, Mert Al, J. M. Chang, S. Kung

{"title":"Differential mutual information forward search for multi-kernel discriminant-component selection with an application to privacy-preserving classification","authors":"Thee Chanyaswad, Mert Al, J. M. Chang, S. Kung","doi":"10.1109/MLSP.2017.8168177","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168177","url":null,"abstract":"In machine learning, feature engineering has been a pivotal stage in building a high-quality predictor. Particularly, this work explores the multiple Kernel Discriminant Component Analysis (mKDCA) feature-map and its variants. However, seeking the right subset of kernels for mKDCA feature-map can be challenging. Therefore, we consider the problem of kernel selection, and propose an algorithm based on Differential Mutual Information (DMI) and incremental forward search. DMI serves as an effective metric for selecting kernels, as is theoretically supported by mutual information and Fisher's discriminant analysis. On the other hand, incremental forward search plays a role in removing redundancy among kernels. Finally, we illustrate the potential of the method via an application in privacy-aware classification, and show on three mobile-sensing datasets that selecting an effective set of kernels for mKDCA feature-maps can enhance the utility classification performance, while successfully preserve the data privacy. Specifically, the results show that the proposed DMI forward search method can perform better than the state-of-the-art, and, with much smaller computational cost, can perform as well as the optimal, yet computationally expensive, exhaustive search.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"23 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73191416","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