Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
On an equivalence between PLSI and LDA
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust DNA microarray image analysis
Machine Vision and Applications
The Latent Process Decomposition of cDNA Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bioinformatics
Geo-located image grouping using latent descriptions
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Hybrid generative-discriminative nucleus classification of renal cell carcinoma
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Biclustering of expression microarray data using affinity propagation
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Renal cancer cell classification using generative embeddings and information theoretic kernels
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
A comparison on score spaces for expression microarray data classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Feature selection using counting grids: application to microarray data
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Exploiting geometry in counting grids
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Hi-index | 0.00 |
Classification of samples in expression microarray experiments represents a crucial task in bioinformatics and biomedicine. In this paper this scenario is addressed by employing a particular class of statistical approaches, called Topic Models. These models, firstly introduced in the text mining community, permit to extract from a set of objects (typically documents) an interpretable and rich description, based on an intermediate representation called topics (or processes). In this paper the expression microarray classification task is cast into this probabilistic context, providing a parallelism with the text mining domain and an interpretation. Two different topic models are investigated, namely the Probabilistic Latent Semantic Analysis (PLSA) and the Latent Dirichlet Allocation (LDA). An experimental evaluation of the proposed methodologies on three standard datasets confirms their effectiveness, also in comparison with other classification methodologies.