Multiple One-Shots for Utilizing Class Label Information

2009 ◽  
Author(s):  
Yaniv Taigman ◽  
Lior Wolf ◽  
Tal Hassner
Keyword(s):  
Class Label ◽  
Label Information

scholarly journals Classification and Representation Joint Learning via Deep Networks

2017 ◽  
Author(s):  
Ya Li ◽  
Xinmei Tian ◽  
Xu Shen ◽  
Dacheng Tao
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Feature Learning ◽  
Classification Performance ◽  
Local Distribution ◽  
Classification Problems ◽  
Class Label ◽  
Channel Network ◽  
Training Samples ◽  
Label Information

Deep learning has been proven to be effective for classification problems. However, the majority of previous works trained classifiers by considering only class label information and ignoring the local information from the spatial distribution of training samples. In this paper, we propose a deep learning framework that considers both class label information and local spatial distribution information between training samples. A two-channel network with shared weights is used to measure the local distribution. The classification performance can be improved with more detailed information provided by the local distribution, particularly when the training samples are insufficient. Additionally, the class label information can help to learn better feature representations compared with other feature learning methods that use only local distribution information between samples. The local distribution constraint between sample pairs can also be viewed as a regularization of the network, which can efficiently prevent the overfitting problem. Extensive experiments are conducted on several benchmark image classification datasets, and the results demonstrate the effectiveness of our proposed method.

scholarly journals Graph Regularized Semi-Supervised Concept Factorization

2012 ◽  
Vol 6-7 ◽  
pp. 583-588
Author(s):  
Yu Qing Shi ◽  
Shi Qiang Du ◽  
Wei Lan Wang
Keyword(s):  
Matrix Factorization ◽  
Matrix Decomposition ◽  
Image Database ◽  
Data Representation ◽  
Face Database ◽  
Class Label ◽  
Intrinsic Geometry ◽  
Label Information ◽  
Concept Factorization ◽  
Non Negative Matrix Factorization

Concept Factorization (CF) is a new matrix decomposition technique for data representation. A modified CF algorithm called Graph Regularized Semi-supervised Concept Factorization (GRSCF) is proposed for addressing the limitations of CF and Local Consistent Concept Factorization (LCCF), which did not consider the geometric structure or the label information of the data. GRSCF preserves the intrinsic geometry of data as regularized term and use the label information as semi-supervised learning, it makes nearby samples with the same class-label are more compact, and nearby classes are separated. Compared with Non-Negative Matrix Factorization (NMF), CNMF, CF and LCCF, experiment results on ORL face database and Coil20 image database have shown that the proposed method achieves better clustering results.

scholarly journals Multi layered Stacked Ensemble Method with Feature Reduction Technique for Multi-Label Classification

2022 ◽  
Vol 2161 (1) ◽  
pp. 012074
Author(s):  
Hemavati ◽  
V Susheela Devi ◽  
R Aparna
Keyword(s):  
Ensemble Learning ◽  
Principal Component ◽  
Initial Step ◽  
Classification Problem ◽  
Feature Reduction ◽  
Training Dataset ◽  
Class Label ◽  
Label Information ◽  
Class Information

Abstract Nowadays, multi-label classification can be considered as one of the important challenges for classification problem. In this case instances are assigned more than one class label. Ensemble learning is a process of supervised learning where several classifiers are trained to get a better solution for a given problem. Feature reduction can be used to improve the classification accuracy by considering the class label information with principal Component Analysis (PCA). In this paper, stacked ensemble learning method with augmented class information PCA (CA PCA) is proposed for classification of multi-label data (SEMML). In the initial step, the dimensionality reduction step is applied, then the number of classifiers have to be chosen to apply on the original training dataset, then the stacking method is applied to it. By observing the results of experiments conducted are showing our proposed method is working better as compared to the existing methods.

BATCH MODE ACTIVE LEARNING FOR GRAPH-BASED SEMI-SUPERVISED LEARNING

2013 ◽  
Vol 27 (07) ◽  
pp. 1351002
Author(s):  
CHEONG HEE PARK
Keyword(s):  
Active Learning ◽  
Supervised Learning ◽  
Selection Process ◽  
Unlabeled Data ◽  
Large Set ◽  
Class Label ◽  
Batch Mode ◽  
Beneficial Effects ◽  
Label Information ◽  
Improved Performance

In semi-supervised learning, when the number of data samples with class label information is very small, information from unlabeled data is utilized in the learning process. Many semi-supervised learning methods have been presented and have exhibited competent performance. Active learning also aims to overcome the shortage of labeled data by obtaining class labels for some selected unlabeled data from experts. However, the selection process for the most informative unlabeled data samples can be demanding when the search is performed over a large set of unlabeled data. In this paper, we propose a method for batch mode active learning in graph-based semi-supervised learning. Instead of acquiring class label information of one unlabeled data sample at a time, we obtain information about several data samples at once, reducing time complexity while preserving the beneficial effects of active learning. Experimental results demonstrate the improved performance of the proposed method.

scholarly journals Pain Expression Recognition Based on pLSA Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Shaoping Zhu
Keyword(s):  
Recognition Accuracy ◽  
Video Sequences ◽  
Expression Recognition ◽  
Class Label ◽  
New Approach ◽  
Visual Words ◽  
Slight Pain ◽  
Pain Expression ◽  
Label Information ◽  
Velocity Information

We present a new approach to automatically recognize the pain expression from video sequences, which categorize pain as 4 levels: “no pain,” “slight pain,” “moderate pain,” and “ severe pain.” First of all, facial velocity information, which is used to characterize pain, is determined using optical flow technique. Then visual words based on facial velocity are used to represent pain expression using bag of words. Final pLSA model is used for pain expression recognition, in order to improve the recognition accuracy, the class label information was used for the learning of the pLSA model. Experiments were performed on a pain expression dataset built by ourselves to test and evaluate the proposed method, the experiment results show that the average recognition accuracy is over 92%, which validates its effectiveness.

Enhanced Parameter-Free Diversity Discriminant Preserving Projections for Face Recognition

2018 ◽  
Vol 32 (10) ◽  
pp. 1856013 ◽  
Author(s):  
Xingzhu Liang ◽  
Yu’e Lin
Keyword(s):  
Face Recognition ◽  
The Novel ◽  
Similarity Matrix ◽  
Face Database ◽  
Class Label ◽  
Data Set ◽  
Scatter Matrix ◽  
Label Information ◽  
Maximum Margin Criterion ◽  
Data Points

The manifold-based learning methods have recently drawn more and more attention in dimension reduction. In this paper, a novel manifold-based learning method named enhanced parameter-free diversity discriminant preserving projections (EPFDDPP) is presented, which effectively avoids the neighborhood parameter selection and characterizes the manifold structure well. EPFDDPP redefines the weighted matrices, the discriminating similarity matrix and the discriminating diversity matrix, respectively. The weighted matrices are computed by the cosine angle distance between two data points and take special consideration of both the local information and the class label information, which are parameterless and favorable for face recognition. After characterizing the discriminating similarity scatter matrix and the discriminating diversity scatter matrix, the novel feature extraction criterion is derived based on maximum margin criterion. Experimental results on the Wine data set, Olivetti Research Laboratory (ORL); AR (face database created by Aleix Martinez and Robert Benavente); and Pose, Illumination, and Expression (PIE) face databases show the effectiveness of the proposed method.

scholarly journals Analysis of correlation based dimension reduction methods

2011 ◽  
Vol 21 (3) ◽  
pp. 549-558 ◽  
Author(s):  
Yong Shin ◽  
Cheong Park
Keyword(s):  
Correlation Analysis ◽  
Dimension Reduction ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Feature Fusion ◽  
Orthogonal Transformation ◽  
Classification Performance ◽  
Class Label ◽  
Label Information ◽  
Reduction Methods

Analysis of correlation based dimension reduction methodsDimension reduction is an important topic in data mining and machine learning. Especially dimension reduction combined with feature fusion is an effective preprocessing step when the data are described by multiple feature sets. Canonical Correlation Analysis (CCA) and Discriminative Canonical Correlation Analysis (DCCA) are feature fusion methods based on correlation. However, they are different in that DCCA is a supervised method utilizing class label information, while CCA is an unsupervised method. It has been shown that the classification performance of DCCA is superior to that of CCA due to the discriminative power using class label information. On the other hand, Linear Discriminant Analysis (LDA) is a supervised dimension reduction method and it is known as a special case of CCA. In this paper, we analyze the relationship between DCCA and LDA, showing that the projective directions by DCCA are equal to the ones obtained from LDA with respect to an orthogonal transformation. Using the relation with LDA, we propose a new method that can enhance the performance of DCCA. The experimental results show that the proposed method exhibits better classification performance than the original DCCA.

Sign in / Sign up

Export Citation Format

Share Document