scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

scholarly journals Towards Application of One-Class Classification Methods to Medical Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Itziar Irigoien ◽  
Basilio Sierra ◽  
Concepción Arenas
Keyword(s):  
State Of The Art ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Biomedical Data ◽  
Vector Data ◽  
Target Class ◽  
Tumor Recognition ◽  
One Class Classification

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

A Novel Region-Based Approach for Automatic Road Extraction from High Resolution Satellite Images

2013 ◽  
Vol 284-287 ◽  
pp. 2998-3003
Author(s):  
Young Gi Byun
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Image ◽  
Road Surface ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Road Extraction ◽  
High Resolution Satellite Images ◽  
High Resolution Satellite Imagery ◽  
One Class Classification

With the constantly increasing public availability of high resolution satellite imagery, interest in automatic road extraction from this imagery has recently increased. Road extraction from high resolution satellite imagery refers to reliable road surface extraction instead of road line extraction because roads in the imagery mostly correspond to an elongated region with a locally constant spectral signature rather than traditional thin lines. This paper proposes a novel automatic road extraction approach that is based on a combination of image segmentation and one-class classification and consists of two main steps. First, the image is segmented using a modified previous segmentation algorithm to achieve more reliable segmentation for road extraction. The key road objects are then automatically extracted from the segmented image to obtain road training samples. Then one-class classification, based on a support vector data description classifier, is carried out to extract the road surface area from the image. The experimental results from a pan-sharpened KOMPSAT-2 satellite image demonstrate the correctness and efficiency of the proposed method for its application to road extraction from high resolution satellite image.

scholarly journals Few-shot Classification of Aerial Scene Images via Meta-learning

2020 ◽  
Author(s):  
Pei Zhang ◽  
YIng Li ◽  
Dong Wang ◽  
Yunpeng Bai
Keyword(s):  
Classification Problem ◽  
Training Data ◽  
Scene Classification ◽  
Shot Classification ◽  
Training Stage ◽  
Testing Stage ◽  
Meta Learning ◽  
Cosine Distance ◽  
Query Sample

CNN-based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale and support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

scholarly journals Assessing Regression-Based Sentiment Analysis Techniques in Financial Texts

2019 ◽  
Author(s):  
Taynan Ferreira ◽  
Francisco Paiva ◽  
Roberto Silva ◽  
Angel Paula ◽  
Anna Costa ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Feature Representation ◽  
Support Vector ◽  
Data Set ◽  
Feature Representations ◽  
Textual Data ◽  
Enormous Amount ◽  
Financial Domain ◽  
Classification Tasks ◽  
The Impact

Sentiment analysis (SA) is increasing its importance due to the enormous amount of opinionated textual data available today. Most of the researches have investigated different models, feature representation and hyperparameters in SA classification tasks. However, few studies were conducted to evaluate the impact of these features on regression SA tasks. In this paper, we conduct such assessment on a financial domain data set by investigating different feature representations and hyperparameters in two important models -- Support Vector Regression (SVR) and Convolution Neural Networks (CNN). We conclude presenting the most relevant feature representations and hyperparameters and how they impact outcomes on a regression SA task.

scholarly journals Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Chao Tan ◽  
Zan Lin
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
K Nearest Neighbor ◽  
Black Rice ◽  
Target Class ◽  
Data Description

Black rice is an important rice species in Southeast Asia. It is a common phenomenon to pass low-priced black rice off as high-priced ones for economic benefit, especially in some remote towns. There is increasing need for the development of fast, easy-to-use, and low-cost analytical methods for authenticity detection. The feasibility to utilize near-infrared (NIR) spectroscopy and support vector data description (SVDD) for such a goal is explored. Principal component analysis (PCA) is used for exploratory analysis and feature extraction. Another two data description methods, i.e., k-nearest neighbor data description (KNNDD) and GAUSS method, are used as the reference. A total of 142 samples from three brands were collected for spectral analysis. Each time, the samples of a brand serve as the target class whereas other samples serve as the outlier class. Based on both the first two principal components (PCs) and original variables, three types of data descriptions were constructed. On average, the optimized SVDD model achieves acceptable performance, i.e., a specificity of 100% and a sensitivity of 94.2% on the independent test set with tight boundary. It indicates that SVDD combined with NIR is feasible and effective for authenticity detection of black rice.

scholarly journals A Support Vector Data Description Approach to NLOS Identification in UWB Positioning

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Shiwei Tian ◽  
Luwen Zhao ◽  
Guangxia Li
Keyword(s):  
Identification Problem ◽  
Classification Problem ◽  
Line Of Sight ◽  
Support Vector ◽  
Delay Spread ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
One Class Classification ◽  
Mean Excess Delay

Non-line-of-sight (NLOS) propagation is one of the most important challenges in radio positioning, and, in recent years, significant attention has been drawn to the identification and mitigation of NLOS signals. This paper focuses on the identification of NLOS signals. The authors consider the NLOS identification problem as a one-class classification problem and apply the support vector data description (SVDD), providing accurate data descriptions utilizing kernel techniques, to perform NLOS identification in ultrawide bandwidth (UWB) positioning. Our work is based on the fact that some features extracted from the received signal waveforms, such as the kurtosis, the mean excess delay spread, and the root mean square delay spread, are different between line-of-sight (LOS) and NLOS signals. Numerical simulations are performed to demonstrate the performance, using a dataset derived from a measurement campaign.

KERNEL WHITENING FOR ONE-CLASS CLASSIFICATION

2003 ◽  
Vol 17 (03) ◽  
pp. 333-347 ◽  
Author(s):  
DAVID M. J. TAX ◽  
PIOTR JUSZCZAK
Keyword(s):  
Feature Space ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Good Representation ◽  
Vector Data ◽  
Data Description ◽  
Unit Variance ◽  
Good Distinction ◽  
One Class Classification ◽  
Target Data

In one-class classification one tries to describe a class of target data and to distinguish it from all other possible outlier objects. Obvious applications are areas where outliers are very diverse or very difficult or expensive to measure, such as in machine diagnostics or in medical applications. In order to have a good distinction between the target objects and the outliers, good representation of the data is essential. The performance of many one-class classifiers critically depends on the scaling of the data and is often harmed by data distributions in (nonlinear) subspaces. This paper presents a simple preprocessing method which actively tries to map the data to a spherical symmetric cluster and is almost insensitive to data distributed in subspaces. It uses techniques from Kernel PCA to rescale the data in a kernel feature space to unit variance. This transformed data can now be described very well by the Support Vector Data Description, which basically fits a hypersphere around the data. The paper presents the methods and some preliminary experimental results.

Compressor Surge Detection Based on Support Vector Data Description

2012 ◽  
Vol 152-154 ◽  
pp. 1545-1549
Author(s):  
Chang Zheng Li ◽  
Yong Lei
Keyword(s):  
Support Vector Machine ◽  
Active Control ◽  
Axial Flow ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Compressor Surge ◽  
Aerodynamic Instability ◽  
One Class Classification

Axial flow compressors work as an indispensable device in industry fields. Surge is a phenomenon of aerodynamic instability, which characterized by disruption of flow. When a compressor works in surge state, the vibration is so intense that it may causes accidents. Detecting surge timely and accurately not only insure safety of compressors but also is a key of active control of aerodynamic instability. Support vector data description (SVDD) is a one-class classification method developed based on the theory of support vector machine (SVM). In this paper, we introduce SVDD into the field of compressor surge detection. It demonstrates that SVDD method can give a warning far ahead of surge.

scholarly journals Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2730 ◽  
Author(s):  
Wei Jiang ◽  
Zheng Wang ◽  
Jesse S. Jin ◽  
Xianfeng Han ◽  
Chunguang Li
Keyword(s):  
Emotion Recognition ◽  
Recognition Performance ◽  
Recognition Task ◽  
Feature Representation ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Acoustic Features ◽  
Acoustic Feature ◽  
Feature Representations ◽  
Feature Unification

Automatic speech emotion recognition is a challenging task due to the gap between acoustic features and human emotions, which rely strongly on the discriminative acoustic features extracted for a given recognition task. We propose a novel deep neural architecture to extract the informative feature representations from the heterogeneous acoustic feature groups which may contain redundant and unrelated information leading to low emotion recognition performance in this work. After obtaining the informative features, a fusion network is trained to jointly learn the discriminative acoustic feature representation and a Support Vector Machine (SVM) is used as the final classifier for recognition task. Experimental results on the IEMOCAP dataset demonstrate that the proposed architecture improved the recognition performance, achieving accuracy of 64% compared to existing state-of-the-art approaches.

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