Some applications of the rank revealing QR factorization
SIAM Journal on Scientific and Statistical Computing
The nature of statistical learning theory
The nature of statistical learning theory
Computing rank-revealing QR factorizations of dense matrices
ACM Transactions on Mathematical Software (TOMS)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
A Semi-Supervised Active Learning Framework for Image Retrieval
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
Laplacian optimal design for image retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical sampling for active learning
Proceedings of the 25th international conference on Machine learning
Unsupervised feature selection for principal components analysis
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Column subset selection, matrix factorization, and eigenvalue optimization
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Uncertainty sampling and transductive experimental design for active dual supervision
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Active learning with statistical models
Journal of Artificial Intelligence Research
Convex experimental design using manifold structure for image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Kernel methods and the exponential family
Neurocomputing
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Active Learning Based on Locally Linear Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Image classification is an important task in computer vision and machine learning. However, it is known that manually labeling images is time-consuming and expensive, but the unlabeled images are easily available. Active learning is a mechanism which tries to determine which unlabeled data points would be the most informative (i.e., improve the classifier the most) if they are labeled and used as training samples. In this paper, we introduce the idea of column subset selection, which aims to select the most representation columns from a data matrix, into active learning and propose a novel active learning algorithm, column subset selection for active learning (CSS"a"c"t"i"v"e). CSS"a"c"t"i"v"e selects the most representative images to label, then the other images are reconstructed by these labeled images. The goal of CSS"a"c"t"i"v"e is to minimize the reconstruction error. Besides, most of the previous active learning approaches are based on linear model, and hence they only consider linear functions. Therefore, they fail to discover the intrinsic geometry in images when the image space is highly nonlinear. Therefore, we provide a kernel-based column subset selection for active learning (KCSS"a"c"t"i"v"e) algorithm which performs the active learning in Reproducing Kernel Hilbert Space (RKHS) instead of the original image space to address this problem. Experimental results on Yale, AT&T and COIL20 data sets demonstrate the effectiveness of our proposed approaches.