milr: Multiple-Instance Logistic Regression with Lasso Penalty

Chen, Ping Yang; Chen, Ching Chuan; Yang, Chun Hao; Chang, Sheng Mao; Lee, Kuo-Jung

doi:10.32614/rj-2017-013

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…In particular, the parameter set (one bias term β 0 ∈ R and β ∈ R 2MN f ) is computed within the following quadratic optimization framework (Chen et al, 2017):…”

Section: Optimization Of T-f Atoms For Mil Representationmentioning

confidence: 99%

Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification

et al. 2020

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Selection of the time-window mainly affects the effectiveness of piecewise feature extraction procedures. We present an enhanced bag-of-patterns representation that allows capturing the higher-level structures of brain dynamics within a wide window range. So, we introduce augmented instance representations with extended window lengths for the short-time Common Spatial Pattern algorithm. Based on multiple-instance learning, the relevant bag-of-patterns are selected by a sparse regression to feed a bag classifier. The proposed higher-level structure representation promotes two contributions: (i) accuracy improvement of bi-conditional tasks, (ii) A better understanding of dynamic brain behavior through the learned sparse regression fits. Using a support vector machine classifier, the achieved performance on a public motor imagery dataset (left-hand and right-hand tasks) shows that the proposed framework performs very competitive results, providing robustness to the time variation of electroencephalography recordings and favoring the class separability.

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“…In particular, the parameter set (one bias term β 0 ∈ R and β ∈ R 2MN f ) is computed within the following quadratic optimization framework (Chen et al, 2017):…”

Section: Optimization Of T-f Atoms For Mil Representationmentioning

confidence: 99%

Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification

et al. 2020

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“…IV. A new value of λ is then selected from a set S λ and Algorithm 2 runs again with hot-started vectors β and v. S λ is constructed as a grid from the initial value of λ = 0.5 up to λ max -a λ value that would already yield a trivial (all-zero) solution of (3) [25], [45]. The hot-starting approach allows re-using the already calculated result of the LBI in a consecutive run with a higher lambda so that a significantly small number of iterations may still lead to convergence.…”

Section: B Using Multiple λ Valuesmentioning

confidence: 99%

“…For λ max one can typically calculate upper bounds (e.g. as described in [25], [45]). However, as evaluated in this section, successful sparse estimation of trend breaks in OTDR profiles can be achieved with values close to 0, giving LB based algorithms an additional advantage in terms of computational complexity over the Adaptive 1 Filter.…”

Section: Complexitymentioning

confidence: 99%

Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Lunglmayr¹,

Amaral²

2019

IEEE Trans. Instrum. Meas.

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Supervision of the physical layer of optical networks is an extremely relevant subject. To detect fiber faults, single-ended solutions such as the Optical Time Domain Reflectometer (OTDR) allow for precise measurements of fault profiles. Combining the OTDR with a signal processing approach for high-dimensional sparse parameter estimation allows for automated and reliable results in reduced time. In this work, a measurement system composed of a Photon-Counting OTDR data acquisition unit and a processing unit based on a Linearized Bregman Iterations algorithm for automatic fault finding is proposed. An in-depth comparative study of the proposed algorithm's fault-finding prowess in the presence of noise is presented. Characteristics such as sensitivity, specificity, processing time, and complexity, are analysed in simulated environments. Real-life measurements that are conducted using the Photon-Counting OTDR subsystem for data acquisition and the Linearized Bregman-based processing unit for automated data analysis demonstrated accurate results. It is concluded that the proposed measurement system is particularly well suited to the task of fault finding. The natural characteristic of the algorithm fosters embedding the solution in digital hardware, allowing for reduced costs and processing time.

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“…Furthermore, Raykar et al [30] introduce a prior on the model parameter and develop a Bayesian MILR algorithm. Chen et al [31] incorporate the LASSO approach into the proposed MILR and provide an efficient computer algorithm for variable selection and estimation.…”

Section: Logistic Regression In Multiple-instance Settingmentioning

confidence: 99%

Logistic Regression Region Weighting for Weakly Supervised Object Localization

Wang

2019

IEEE Access

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In this paper, we address the problem of weakly supervised object localization using region weighting. For a weakly labelled image/video, the inside regions have different relevance to its semantic label. We first over-segment an image/video to get super-pixel/voxel regions, and assign each region with a latent weight to represent its support to the semantic label, then regress the weights to right values by optimizing the classification according to the weak labels. We adopt logistic regression as our base model due to its good performance in multiple-instance setting. The latent region weights are incorporated into the objective function as an interpretation of region combination at feature-level. The weights and the model parameters are optimized in an alternate manner. With the updates of the weights, the model is trained on the semantic regions independently of the background, therefore the learned model is capable of distinguishing object and non-object regions, and generating irregular-shape object localization. The method overcomes the limitations of applying multiple-instance learning to visual object localization. Experimental results on three datasets validates the effectiveness of the proposed method.

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milr: Multiple-Instance Logistic Regression with Lasso Penalty

Cited by 11 publications

References 13 publications

Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification

Enhanced Multiple Instance Representation Using Time-Frequency Atoms in Motor Imagery Classification

Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis

Logistic Regression Region Weighting for Weakly Supervised Object Localization

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