Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new discriminative kernel from probabilistic models
Neural Computation
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Nonextensive Information Theoretic Kernels on Measures
The Journal of Machine Learning Research
Expression microarray classification using topic models
Proceedings of the 2010 ACM Symposium on Applied Computing
Geo-located image grouping using latent descriptions
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Information theoretical Kernels for generative embeddings based on hidden Markov models
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Biclustering of Expression Microarray Data with Topic Models
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Computational TMA analysis and cell nucleus classification of renal cell carcinoma
Proceedings of the 32nd DAGM conference on Pattern recognition
Brain morphometry by probabilistic latent semantic analysis
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
A multiple Kernel learning algorithm for cell nucleus classification of renal cell carcinoma
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
| Hi-index | 0.00 |
In this paper, we propose a hybrid generative/discriminative classification scheme and apply it to the detection of renal cell carcinoma (RCC) on tissue microarray (TMA) images. In particular we use probabilistic latent semantic analysis (pLSA) as a generative model to perform generative embedding onto the free energy score space (FESS). Subsequently, we use information theoretic kernels on these embeddings to build a kernel based classifier on the FESS. We compare our results with support vector machines based on standard linear kernels and RBF kernels; and with the nearest neighbor (NN) classifier based on the Mahalanobis distance using a diagonal covariance matrix. We conclude that the proposed hybrid approach achieves higher accuracy, revealing itself as a promising approach for this class of problems.