Huber collaborative representation for robust multiclass classification

2019 ◽  
Vol 17 (04) ◽  
pp. 1950020 ◽  
Author(s):  
Cuiming Zou ◽  
Yuan Yan Tang ◽  
Yulong Wang ◽  
Zhenghua Luo
Keyword(s):  
Robust Statistics ◽  
Classification Problem ◽  
Multiclass Classification ◽  
Classification Performance ◽  
Collaborative Representation ◽  
Proposed Model ◽  
The Mean ◽  
Classification Tasks ◽  
Real World Datasets ◽  
The Cost

Recent advances have shown a great potential of collaborative representation (CR) for multiclass classification. However, conventional CR-based classification methods adopt the mean square error (MSE) criterion as the cost function, which is sensitive to gross corruption and outliers. To address this limitation, inspired by the success of robust statistics, we develop a Huber collaborative representation-based classification (HCRC) method for robust multiclass classification. Concretely, we cast the classification problem as a Huber collaborative representation problem with the Huber estimator. Our another contribution is to design an efficient half-quadratic (HQ) algorithm with guaranteed convergence to solve the proposed model efficiently. Furthermore, we also give a theoretical analysis of the classification performance of HCRC. Experiments on real-world datasets corroborate that HCRC is an effective and robust algorithm for multiclass classification tasks.

scholarly journals Improvements on Learning Kernel Extended Dictionary for Face Recognition

2020 ◽  
Vol 34 (4) ◽  
pp. 387-394
Author(s):  
Soodabeh Amanzadeh ◽  
Yahya Forghani ◽  
Javad Mahdavi Chabok
Keyword(s):  
Sparse Representation ◽  
Face Image ◽  
Collaborative Representation ◽  
Training Images ◽  
Input Face ◽  
Proposed Model ◽  
Eigen Values ◽  
New Type ◽  
The Mean ◽  
Better Than

Kernel extended dictionary learning model (KED) is a new type of Sparse Representation for Classification (SRC), which represents the input face image as a linear combination of dictionary set and extended dictionary set to determine the input face image class label. Extended dictionary is created based on the differences between the occluded images and non-occluded training images. There are four defaults to make about KED: (1) Similar weights are assigned to the principle components of occlusion variations in KED model, while the principle components of the occlusion variations have different weights, which are proportional to the principle components Eigen-values. (2) Reconstruction of an occluded image is not possible by combining only non-occluded images and the principle components (or the directions) of occlusion variations, but it requires the mean of occlusion variations. (3) The importance and capability of main dictionary and extended dictionary in reconstructing the input face image is not the same, necessarily. (4) KED Runtime is high. To address these problems or challenges, a novel mathematical model is proposed in this paper. In the proposed model, different weights are assigned to the principle components of occlusion variations; different weights are assigned to the main dictionary and extended dictionary; an occluded image is reconstructed by non-occluded images and the principle components of occlusion variations, and also the mean of occlusion variations; and collaborative representation is used instead of sparse representation to enhance the runtime. Experimental results on CAS-PEAL subsets showed that the runtime and accuracy of the proposed model is about 1% better than that of KED.

scholarly journals Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification

2019 ◽  
Vol 33 ◽  
pp. 3304-3311
Author(s):  
Chen Chen ◽  
Haobo Wang ◽  
Weiwei Liu ◽  
Xingyuan Zhao ◽  
Tianlei Hu ◽  
...  
Keyword(s):  
Learning Model ◽  
Two Stage ◽  
Second Stage ◽  
Proposed Model ◽  
Latent Space ◽  
Label Correlations ◽  
Factorization Machine ◽  
Classification Tasks ◽  
Real World Datasets ◽  
Embedding Methods

Label embedding has been widely used as a method to exploit label dependency with dimension reduction in multilabel classification tasks. However, existing embedding methods intend to extract label correlations directly, and thus they might be easily trapped by complex label hierarchies. To tackle this issue, we propose a novel Two-Stage Label Embedding (TSLE) paradigm that involves Neural Factorization Machine (NFM) to jointly project features and labels into a latent space. In encoding phase, we introduce a Twin Encoding Network (TEN) that digs out pairwise feature and label interactions in the first stage and then efficiently learn higherorder correlations with deep neural networks (DNNs) in the second stage. After the codewords are obtained, a set of hidden layers is applied to recover the output labels in decoding phase. Moreover, we develop a novel learning model by leveraging a max margin encoding loss and a label-correlation aware decoding loss, and we adopt the mini-batch Adam to optimize our learning model. Lastly, we also provide a kernel insight to better understand our proposed TSLE. Extensive experiments on various real-world datasets demonstrate that our proposed model significantly outperforms other state-ofthe-art approaches.

scholarly journals AMN: Attention Metric Network for One-Shot Remote Sensing Image Scene Classification

2020 ◽  
Vol 12 (24) ◽  
pp. 4046
Author(s):  
Xirong Li ◽  
Fangling Pu ◽  
Rui Yang ◽  
Rong Gui ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Classification Problem ◽  
Remote Sensing Image ◽  
Similarity Measurement ◽  
Scene Classification ◽  
Feature Maps ◽  
Training Strategy ◽  
Measurement Results ◽  
Classification Tasks ◽  
The Cost

In recent years, deep neural network (DNN) based scene classification methods have achieved promising performance. However, the data-driven training strategy requires a large number of labeled samples, making the DNN-based methods unable to solve the scene classification problem in the case of a small number of labeled images. As the number and variety of scene images continue to grow, the cost and difficulty of manual annotation also increase. Therefore, it is significant to deal with the scene classification problem with only a few labeled samples. In this paper, we propose an attention metric network (AMN) in the framework of the few-shot learning (FSL) to improve the performance of one-shot scene classification. AMN is composed of a self-attention embedding network (SAEN) and a cross-attention metric network (CAMN). In SAEN, we adopt the spatial attention and the channel attention of feature maps to obtain abundant features of scene images. In CAMN, we propose a novel cross-attention mechanism which can highlight the features that are more concerned about different categories, and improve the similarity measurement performance. A loss function combining mean square error (MSE) loss with multi-class N-pair loss is developed, which helps to promote the intra-class similarity and inter-class variance of embedding features, and also improve the similarity measurement results. Experiments on the NWPU-RESISC45 dataset and the RSD-WHU46 dataset demonstrate that our method achieves the state-of-the-art results on one-shot remote sensing image scene classification tasks.

scholarly journals Independent Random Recurrent Neural Networks for Infrared Spatial Point Targets Classification

2019 ◽  
Vol 9 (21) ◽  
pp. 4622 ◽  
Author(s):  
Wu ◽  
Lu ◽  
Hu ◽  
Zhao
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Radiation Intensity ◽  
Infrared Radiation ◽  
Classification Problem ◽  
Classification Performance ◽  
Surveillance Systems ◽  
Spatial Point ◽  
Classification Tasks ◽  
Comprehensive Classification

Exo-atmospheric infrared (IR) point target discrimination is an important research topic of space surveillance systems. It is difficult to describe the characteristic information of the shape and micro-motion states of the targets and to discriminate different targets effectively by the characteristic information. This paper has constructed the infrared signature model of spatial point targets and obtained the infrared radiation intensity sequences dataset of different types of targets. This paper aims to design an algorithm for the classification problem of infrared radiation intensity sequences of spatial point targets. Recurrent neural networks (RNNs) are widely used in time series classification tasks, but face several problems such as gradient vanishing and explosion, etc. In view of shortcomings of RNNs, this paper proposes an independent random recurrent neural network (IRRNN) model, which combines independent structure RNNs with randomly weighted RNNs. Without increasing the training complexity of network learning, our model solves the problem of gradient vanishing and explosion, improves the ability to process long sequences, and enhances the comprehensive classification performance of the algorithm effectively. Experiments show that the IRRNN algorithm performs well in classification tasks and is robust to noise.

scholarly journals Classification of Reinforcement Costs of Masonry Walls Using Hybrid Extreme Gradient Boosting and Softmax

2021 ◽  
Author(s):  
Melda Alkan Çakıroğlu ◽  
◽  
Ahmet Ali Süzen ◽  
Keyword(s):  
Machine Learning ◽  
Rural Areas ◽  
Polypropylene Fiber ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
Force Time ◽  
The Cost

It has been built for centuries as housing and animal shelters, especially in rural areas, due to the advantages of masonry buildings being economical, being built with local materials, and not requiring skilled labor. The walls, which are the bearing elements of masonry structures, are formed by placing stones, bricks, or blocks on top of each other with a binding mortar. In this study, a model with the XGBoost algorithm, which is a tree-based classification algorithm, is proposed to scale cost of the samples reinforced with welded wire reinforcement/polypropylene fiber added dry mix shotcrete. The model executes cost classification based on concrete, steel mesh, steel, epoxy, fiber and workmanship independent parameters. A softmax function was incorporated into the model for classification. A complexity matrix was produced to evaluate classification performance of model. Also, it was compared to other machine learning algorithms. The model yielded higher accuracy and lower false-positive rates. As a result, the proposed model can make better estimates in cost classification compared to other machine learning methods. In conclusion, using the classification ability of the model, it is aimed to measure the cost effect in the construction process that calls for high labor force, time and cost.

scholarly journals A HIERARCHICAL CLASSIFIER FOR MULTICLASS PROSTATE HISTOPATHOLOGY IMAGE GLEASON GRADING

2018 ◽  
Author(s):  
Dheeb Albashish ◽  
Shahnorbanun Sahran ◽  
Azizi Abdullah ◽  
Afzan Adam ◽  
Mohammed Alweshah
Keyword(s):  
Domain Knowledge ◽  
High Efficiency ◽  
Binary Classification ◽  
Class Imbalance ◽  
Classification Problem ◽  
Multiclass Classification ◽  
Classification Performance ◽  
Divide And Conquer ◽  
Multiclass Problem ◽  
Histopathology Images

Automated classification of prostate histopathology images includes the identification of multiple classes, such as benign and cancerous (grades 3 & 4). To address the multiclass classification problem in prostate histopathology images, breakdown approaches are utilized, such as one-versus-one (OVO) and one- versus-all (Ovall). In these approaches, the multiclass problem is decomposed into numerous binary subtasks, which are separately addressed. However, OVALL introduces an artificial class imbalance, which degrades the classification performance, while in the case of OVO, the correlation between different classes not regarded as a multiclass problem is broken into multiple independent binary problems. This paper proposes a new multiclass approach called multi-level (hierarchical) learning architecture (MLA). It addresses the binary classification tasks within the framework of a hierarchical strategy. It does so by accounting for the interaction between several classes and the domain knowledge. The proposed approach relies on the ‘divide-and-conquer’ principle by allocating each binary task into two separate subtasks; strong and weak, based on the power of the samples in each binary task. Conversely, the strong samples include more information about the considered task, which motivates the production of the final prediction. Experimental results on prostate histopathological images illustrated that the MLA significantly outperforms the Ovall and OVO approaches when applied to the ensemble framework. The results also confirmed the high efficiency of the ensemble framework with the MLA scheme in dealing with the multiclass classification problem.

scholarly journals MODELO MATEMÁTICO PARA DETERMINAÇÃO DO DIÂMETRO ECONÔMICO DE TUBULAÇÕES DE RECALQUE

Irriga ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 208-219
Author(s):  
Bruna Dalcin Pimenta ◽  
Adroaldo Dias Robaina ◽  
Marcia Xavier Peiter ◽  
Jhosefe Bruning ◽  
Miguel Chaiben Neto ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Recovery Factor ◽  
Water Pumping ◽  
General Index ◽  
Proposed Model ◽  
Model Efficiency ◽  
Quadratic Deviation ◽  
The Mean ◽  
The Cost ◽  
Santa Maria

MODELO MATEMÁTICO PARA DETERMINAÇÃO DO DIÂMETRO ECONÔMICO DE TUBULAÇÕES DE RECALQUE     BRUNA DALCIN PIMENTA1; ADROALDO DIAS ROBAINA1; MARCIA XAVIER PEITER1; JHOSEFE BRUNING1; MIGUEL CHAIBEN NETO1 E YESICA RAMIREZ FLORES1   1Programa de Pós-Graduação em Engenharia Agrícola, Universidade Federal de Santa Maria/RS, Av. Roraima, nº 1000, Bairro Camobi, Santa Maria/RS, Brasil, CEP  97105-900, [email protected], [email protected], [email protected], [email protected], [email protected], [email protected].     1 RESUMO   O conhecimento do diâmetro econômico de tubulações é de grande importância em dimensionamentos de sistemas de recalque, pois, assim, é possível otimizar os custos de sistemas de irrigação. O objetivo deste trabalho foi propor um modelo matemático para o cálculo do diâmetro econômico de tubulações de recalque utilizadas em sistemas de bombeamento de água para irrigação. Na modelagem matemática do cálculo do diâmetro econômico foi utilizada, para mensurar a perda de carga, a equação de Hazen-Williams, levando em consideração o custo das tubulações, o custo do conjunto motobomba, o custo da energia consumida e o fator de recuperação do capital. Nas simulações foram utilizadas vazões de 25 a 500 l s-1, em intervalos de 25 l s-1, fator de recuperação do capital de 10, 15 e 20%, e preço da energia consumida de US$0,09, US$0,10 e US$0,11 kWh-1, para tubos de aço com espessura de 2,00mm, 2,65mm e 3,00mm. Para validação do modelo proposto, por meio da comparação com um modelo que utiliza a formulação de Darcy-Weisbach, foram utilizadas a raiz média do desvio quadrático, a raiz média do desvio quadrático normalizado, a eficiência do modelo e o índice geral de desempenho do modelo. Os resultados indicam que o modelo proposto apresenta índice geral de desempenho próximos a 1,00 em todos os cenários, podendo, dessa forma, ser utilizado com precisão para determinação do diâmetro econômico de tubulações de recalque sem necessidade do uso de equações que necessitam de processos iterativos para sua solução.   Palavras-chave: modelagem, irrigação, consumo de energia, Hazen-Williams.     PIMENTA, B. D.; ROBAINA, A. D.; PEITER, M. X.; BRUNING, J.; CHAIBEN NETO, M. E FLORES, Y. R. MATHEMATICAL MODEL FOR DETERMINING THE ECONOMIC DIAMETER OF PRESSURIZED PIPES     2 ABSTRACT   The knowledge of the economic diameter of pipelines is of great importance in the design of water pumping systems because it makes possible to optimize  costs of irrigation systems. The objective of this work was to propose a mathematical model for the calculation of the economic diameter of pressurized pipes used in water pumping systems for irrigation. In the economic diameter calculation mathematical modeling  Hazen-Williams equation was used to measure loss of load, taking into account the pipes’ cost, the cost of the motor pump, the cost of the energy consumed and the recovery factor of the capital. In the simulations, flow rates of 25 to 500 l s-1 were used, in intervals of 25 l s-1, capital recovery factor of 10, 15 and 20%, and energy price of US$0.09, US$0.10 and US$0.11 kWh-1, for steel pipes with thickness of 2.00mm, 2.65mm and 3.00mm. For validation of the proposed model, through comparison with a model using Darcy-Weisbach formulation, the mean root of the quadratic deviation, the mean root of the normalized quadratic deviation, the model efficiency and the overall performance index of the model were used. The results indicate that the proposed model presents a general index of performance close to 1.00 in all scenarios and can therefore be used with precision to determine the economic diameter of pressurized pipes without the use of equations that require processes solutions.   Keywords: modeling, irrigation, energy consumption, Hazen-Williams.

Design and development of manually operated gladiolus planter

2018 ◽  
Vol 5 (01) ◽  
Author(s):  
TAPAN K. KHURA ◽  
H. L. KUSHWAHA ◽  
SATISH D LANDE ◽  
PKSAHOO . ◽  
INDRA L . KUSHWAHA
Keyword(s):  
Coefficient Of Variation ◽  
Field Capacity ◽  
Power Source ◽  
Forward Speed ◽  
Cereal Crop ◽  
Maximum Coefficient ◽  
Coefficient Of Uniformity ◽  
The Mean ◽  
The Cost ◽  
Immense Potential

Floriculture is an age-old farming activity in India having immense potential for generating selfemployment and income to farmers. However, the cost of cultivation of flower is high as compared to cereal crop. Level of mechanization for different field operations is one but foremost reason for the higher cost of cultivation. As most of the Indian farmers are marginal and small, a need for manually operated gladiolus planter was felt. The geometric properties of gladiolus corm were determined for designing the seed metering system and seed hopper of the planter. The planter was evaluated in the field when pulled by two persons as a power source and guided by a person. The coefficient of variation and highest deviation from the mean spacing was observed as 12.93% and 2.65cm respectively. The maximum coefficient of uniformity of 90.59% was observed for a nominal corm spacing of 15cm at 0.56 kmh-1 forward speed. An average MISS percentage was observed as 2.65 and 2.25 for nominal corm spacing of 15 and 20 cm. The multiple index was zero for two levels corm spacing and forward speed of operation. The QFI was found in the range of 97.2 and 97.9 percent. The average field capacity of the planter was observed as 0.02 hah-1.The average draft requirement of the planter was found as 821 ± 50.3 N.

Simple explicit model of liquid mean flow in region above axial impeller

1985 ◽  
Vol 50 (11) ◽  
pp. 2396-2410
Author(s):  
Miloslav Hošťálek ◽  
Ivan Fořt
Keyword(s):  
Vortex Flow ◽  
Flow Model ◽  
Quantitative Agreement ◽  
Mean Flow ◽  
Flat Bottom ◽  
Proposed Model ◽  
Minimum Number ◽  
The Mean ◽  
Model Equations ◽  
Radial Circulation

The study describes a method of modelling axial-radial circulation in a tank with an axial impeller and radial baffles. The proposed model is based on the analytical solution of the equation for vortex transport in the mean flow of turbulent liquid. The obtained vortex flow model is tested by the results of experiments carried out in a tank of diameter 1 m and with the bottom in the shape of truncated cone as well as by the data published for the vessel of diameter 0.29 m with flat bottom. Though the model equations are expressed in a simple form, good qualitative and even quantitative agreement of the model with reality is stated. Apart from its simplicity, the model has other advantages: minimum number of experimental data necessary for the completion of boundary conditions and integral nature of these data.

scholarly journals Modeling the Excess Velocity of Low-Viscous Taylor Droplets in Square Microchannels

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 162 ◽  
Author(s):  
Thorben Helmers ◽  
Philip Kemper ◽  
Jorg Thöming ◽  
Ulrich Mießner
Keyword(s):  
High Speed ◽  
Process Intensification ◽  
Continuous Phase ◽  
Channel Cross Section ◽  
Optimization Approach ◽  
Mean Flow ◽  
Bypass Flow ◽  
Wall Film ◽  
Proposed Model ◽  
The Mean

Microscopic multiphase flows have gained broad interest due to their capability to transfer processes into new operational windows and achieving significant process intensification. However, the hydrodynamic behavior of Taylor droplets is not yet entirely understood. In this work, we introduce a model to determine the excess velocity of Taylor droplets in square microchannels. This velocity difference between the droplet and the total superficial velocity of the flow has a direct influence on the droplet residence time and is linked to the pressure drop. Since the droplet does not occupy the entire channel cross-section, it enables the continuous phase to bypass the droplet through the corners. A consideration of the continuity equation generally relates the excess velocity to the mean flow velocity. We base the quantification of the bypass flow on a correlation for the droplet cap deformation from its static shape. The cap deformation reveals the forces of the flowing liquids exerted onto the interface and allows estimating the local driving pressure gradient for the bypass flow. The characterizing parameters are identified as the bypass length, the wall film thickness, the viscosity ratio between both phases and the C a number. The proposed model is adapted with a stochastic, metaheuristic optimization approach based on genetic algorithms. In addition, our model was successfully verified with high-speed camera measurements and published empirical data.

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