Multiple Ordinal Regression by Maximizing the Sum of Margins

Hamsici, Onur C.; Martı́nez, Aleix M.

doi:10.1109/tnnls.2015.2477321

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…As an alternative to classification, we consider the TCC retrieval as ordinal regression since TCC classes have a natural order. There are a few competing approaches for solving ordinal regression with artificial neural networks [97][98][99][100][101][102][103]. As mentioned above, the architecture of a network does not depend on its loss function.…”

Section: Tcc Retrieval As Ordinal Regressionmentioning

confidence: 99%

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On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

et al. 2021

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Total Cloud Cover (TCC) retrieval from ground-based optical imagery is a problem that has been tackled by several generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there has been no considerable progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose a framework for the assessment of the algorithms’ characteristics. We present several new algorithms that are based on deep learning techniques: A model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million all-sky optical images of the visible sky dome taken in various regions of the world ocean. The research campaigns that contributed to the DASIO collection took place in the Atlantic ocean, the Indian ocean, the Red and Mediterranean seas, and the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and test subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver a superior quality compared to all previously published approaches. As a key result, we demonstrate a considerable drop in the ability to generalize the training data in the case of a strong covariate shift between the training and test subsets of imagery which may occur in the case of region-aware subsampling.

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Section: Tcc Retrieval As Ordinal Regressionmentioning

confidence: 99%

“…It defines the behavior of the network rather than its architecture. Thus, one may apply any of the approaches mentioned in the studies [64,[97][98][99][100][101][102][103] with their own network architecture. In our study, we exploit PyramidNet within the approach of ordinal regression.…”

Section: Tcc Retrieval As Ordinal Regressionmentioning

confidence: 99%

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

et al. 2021

View full text Add to dashboard Cite

show abstract

“…As an alternative, we consider the TCC retrieval problem as ordinal regression, since TCC classes has natural order. There are a few competing approaches for solving ordinal regression with artificial neural networks [90][91][92][93][94][95][96]. As mentioned above, the architecture of a network does not depend on its loss function.…”

Section: Tcc Retrieval As Ordinal Regressionmentioning

confidence: 99%

“…It will define the behavior of the network rather than its architecture. Thus, one may apply any of the approaches mentioned in the studies [57,[90][91][92][93][94][95][96] with own network architecture. In our study, we exploit PyramidNet within the approach of ordinal regression.…”

Section: Tcc Retrieval As Ordinal Regressionmentioning

confidence: 99%

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

Krinitskiy¹,

Aleksandrova²,

Verezemskaya³

et al. 2020

Preprint

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Total cloud cover (TCC) retrieval from ground-based optical imagery is a problem being tackled by a few generations of researchers. The number of human-designed algorithms for the estimation of TCC grows every year. However, there is not very much progress in terms of quality, mostly due to the lack of systematic approach to the design of the algorithms, to the assessment of their generalization ability, and to the assessment of the TCC retrieval quality. In this study, we discuss the optimization nature of data-driven schemes for TCC retrieval. In order to compare the algorithms, we propose the framework for the assessment of the algorithms characteristics. We present several new algorithms that are based on deep learning techniques: a model for outliers filtering, and a few models for TCC retrieval from all-sky imagery. For training and assessment of data-driven algorithms of this study, we present the Dataset of All-Sky Imagery over the Ocean (DASIO) containing over one million of all-sky optical images of visible sky dome taken in various regions of the World Ocean. The research campaigns contributed to DASIO collection took place in the Atlantic ocean, the Indian ocean the Red and Mediterranean seas, and also in the Arctic ocean. Optical imagery collected during these missions are accompanied by standard meteorological observations of cloudiness characteristics made by experienced observers. We assess the generalization ability of the presented models in several scenarios that differ in terms of the regions selected for the train and validation subsets. As a result, we demonstrate that our models based on convolutional neural networks deliver superior quality compared to all previously published schemes. As a key result, we demonstrate considerable drop of the ability to generalize the training data in case of strong covariate shift between training and validation subsets of imagery which may take place in case of region-aware subsampling.

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“…As to our knowledge of machine learning, binary classifier is to perfectly separate the positive and negative samples, where the margin restriction is to maximize the distance of the nearest two samples from the two different categories, whereas learning the ranking functions is to rank the training samples in terms of the attribute strengths, so its margin restriction is to maximize the distance of the nearest two samples during the ranking [28,29]. Therefore, the ranking functions are better to reflect the relationships of the relative strengths between the attributes of the different categories.…”

Section: Ranking Function Learning Based On Hybrid Relativementioning

confidence: 99%

Hybrid Relative Attributes Based on Sparse Coding for Zero‐Shot Image Classification

Sun

Xiao

2019

Mathematical Problems in Engineering

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As a specific case of image recognition, zero-shot image classification is difficult to solve since its training set cannot cover all the categories of the testing set. From the view point of human vision recognition, the objects can be recognized through the visible and nameable description to the properties. To be the semantic description of the object property, attributes can be taken as a bridge between the seen and unseen categories, which are capable of using into zero-shot image classification. There are mainly binary attributes and relative attributes for zero-shot classification, where the relative attributes have the ability to catch more general sematic relationship than the binary ones. But relative attributes do not always work in zero-shot classification for those categories having similar relative strength attributes. Aiming at solving the defect of the relative attributes in describing the similar categories, we propose to construct the Hybrid Relative Attributes based on Sparse Coding (SC-HRA). First, sparse coding is implemented on low-level features to get nonsemantic relative attributes, which are the necessary complement to the existing relative attributes. After that, they are integrated with the relative attributes to form the hybrid relative attributes (HRA). HRA ranking functions are then learned by the relative attribute learning. Finally, the class label is obtained according to the predicted ranking results of HRA and the ranking relations of HRA among the categories. To verify the effectiveness of SC-HRA, the extensive experiments are conducted on the datasets of faces and natural scenes. The results show that SC-HRA acquires the higher classification accuracy and AUC value.

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Multiple Ordinal Regression by Maximizing the Sum of Margins

Cited by 13 publications

References 37 publications

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

Hybrid Relative Attributes Based on Sparse Coding for Zero‐Shot Image Classification

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