An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Dimension Reduction in Text Classification with Support Vector Machines
The Journal of Machine Learning Research
Supervised probabilistic principal component analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Conventional dimensionality reduction algorithms such as principle component analysis (PCA) and non-negative matrix factorization (NMF) are unsupervised. Supervised probabilistic PCA (SPPCA) can utilize label information. However, this information is usually treated as regression targets rather than discrete nominal labels. We propose a classification probabilistic PCA (CPPCA) which is an extension of probabilistic PCA. Unlike SPPCA, the label class information is turned into a class probabilistic function by using a sigmoidal function. As the posterior distribution of latent variables are non-Gaussian, we use Laplace approximation with Expectation Maximization (EM) to obtain the solution. The formulation is applied to a domain adaptation classification problem where the labeled training data and unlabeled test data come from different but related domains. Experimental results show that the proposed model has accuracy over conventional probabilistic PCA, SPPCA and its semi-supervised version. It has similar performance when compared with popular dedicated algorithms for domain adaptation, the structural correspondence learning (SCL) and its variants.