The Experts below are selected from a list of 33813 Experts worldwide ranked by ideXlab platform
Mark Girolami - One of the best experts on this subject based on the ideXlab platform.
-
sparse Multinomial Logistic Regression via bayesian l1 regularisation
Neural Information Processing Systems, 2006Co-Authors: Gavin C Cawley, Nicola L C Talbot, Mark GirolamiAbstract:Multinomial Logistic Regression provides the standard penalised maximum-likelihood solution to multi-class pattern recognition problems. More recently, the development of sparse Multinomial Logistic Regression models has found application in text processing and microarray classification, where explicit identification of the most informative features is of value. In this paper, we propose a sparse Multinomial Logistic Regression method, in which the sparsity arises from the use of a Laplace prior, but where the usual regularisation parameter is integrated out analytically. Evaluation over a range of benchmark datasets reveals this approach results in similar generalisation performance to that obtained using cross-validation, but at greatly reduced computational expense.
-
NIPS - Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation
2006Co-Authors: Gavin C Cawley, Nicola L C Talbot, Mark GirolamiAbstract:Multinomial Logistic Regression provides the standard penalised maximum-likelihood solution to multi-class pattern recognition problems. More recently, the development of sparse Multinomial Logistic Regression models has found application in text processing and microarray classification, where explicit identification of the most informative features is of value. In this paper, we propose a sparse Multinomial Logistic Regression method, in which the sparsity arises from the use of a Laplace prior, but where the usual regularisation parameter is integrated out analytically. Evaluation over a range of benchmark datasets reveals this approach results in similar generalisation performance to that obtained using cross-validation, but at greatly reduced computational expense.
Stephen Marshall - One of the best experts on this subject based on the ideXlab platform.
-
Extreme sparse Multinomial Logistic Regression: a fast and robust framework for hyperspectral image classification
Remote Sensing, 2017Co-Authors: Faxian Cao, Zhijing Yang, Jinchang Ren, Wing-kuen Ling, Huimin Zhao, Stephen MarshallAbstract:Although sparse Multinomial Logistic Regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse Multinomial Logistic Regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the Logistic Regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.
Faxian Cao - One of the best experts on this subject based on the ideXlab platform.
-
Extreme sparse Multinomial Logistic Regression: a fast and robust framework for hyperspectral image classification
Remote Sensing, 2017Co-Authors: Faxian Cao, Zhijing Yang, Jinchang Ren, Wing-kuen Ling, Huimin Zhao, Stephen MarshallAbstract:Although sparse Multinomial Logistic Regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse Multinomial Logistic Regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the Logistic Regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.
-
extreme sparse Multinomial Logistic Regression a fast and robust framework for hyperspectral image classification
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Faxian Cao, Zhijing Yang, Jinchang Ren, Wing-kuen LingAbstract:Although the sparse Multinomial Logistic Regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse Multinomial Logistic Regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the Logistic Regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.
Jose M. Bioucas-dias - One of the best experts on this subject based on the ideXlab platform.
-
A Subspace-Based Multinomial Logistic Regression for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2014Co-Authors: Khodadadzadeh, Antonio Plaza, Jose M. Bioucas-diasAbstract:In this letter, we propose a Multinomial-Logistic-Regression method for pixelwise hyperspectral classification. The feature vectors are formed by the energy of the spectral vectors projected on class-indexed subspaces. In this way, we model not only the linear mixing process that is often present in the hyperspectral measurement process but also the nonlinearities that are separable in the feature space defined by the aforementioned feature vectors. Our experimental results have been conducted using both simulated and real hyperspectral data sets, which are collected using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Reflective Optics System Imaging Spectrographic (ROSIS) system. These results indicate that the proposed method provides competitive results in comparison with other state-of-the-art approaches.
-
Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Jose M. Bioucas-dias, Antonio PlazaAbstract:In this letter, we propose a new semisupervised learning (SSL) algorithm for remotely sensed hyperspectral image classification. Our main contribution is the development of a new soft sparse Multinomial Logistic Regression model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. The proposed algorithm represents an innovative contribution with regard to conventional SSL algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with real hyperspectral images, in which comparisons with conventional semisupervised self-learning algorithms with hard labels are carried out. In such comparisons, our method exhibits state-of-the-art performance.
Khodadadzadeh - One of the best experts on this subject based on the ideXlab platform.
-
IGARSS - Subspace Multinomial Logistic Regression Ensemble for Classification of Hyperspectral Images
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Khodadadzadeh, Pedram Ghamisi, Cecilia Contreras, Richard GloaguenAbstract:Exploiting multiple complementary classifiers in an ensemble framework has shown to be effective for improving hyperspectral image classification results, specially when the training samples are limited. With a different principle and based on this assumption that hyperspectal feature vectors effectively lie in a low-dimensional subspace, the subspace-based techniques have shown great classification performance. In this work, we propose a new ensemble method for accurate classification of hyperspectral images, which exploits the concept of subspace projection. For this purpose, we extend the subspace Multinomial Logistic Regression classifier (MLRsub) to learn from multiple random subspaces for each class. More specifically, we impose diversity in constructing MLRsub by randomly selecting bootstrap samples from the training set and subsets of the original hyperspectral feature space, which lead to generate different class subspace features. Experimental results, conducted on two real hyperspectral data sets, indicate that the proposed method provides significant classification results in comparison with other state-of-the-art approaches.
-
A Subspace-Based Multinomial Logistic Regression for Hyperspectral Image Classification
IEEE Geoscience and Remote Sensing Letters, 2014Co-Authors: Khodadadzadeh, Antonio Plaza, Jose M. Bioucas-diasAbstract:In this letter, we propose a Multinomial-Logistic-Regression method for pixelwise hyperspectral classification. The feature vectors are formed by the energy of the spectral vectors projected on class-indexed subspaces. In this way, we model not only the linear mixing process that is often present in the hyperspectral measurement process but also the nonlinearities that are separable in the feature space defined by the aforementioned feature vectors. Our experimental results have been conducted using both simulated and real hyperspectral data sets, which are collected using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Reflective Optics System Imaging Spectrographic (ROSIS) system. These results indicate that the proposed method provides competitive results in comparison with other state-of-the-art approaches.
