Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
| Hi-index | 0.00 |
Similarity search and data mining often rely on distance or similarity functions in order to provide meaningful results and semantically meaningful patterns. However, standard distance measures likeLp-norms are often not capable to accurately mirror the expected similarity between two objects. To bridge the so-called semantic gap between feature representation and object similarity, the distance function has to be adjusted to the current application context or user. In this paper, we propose a new probabilistic framework for estimating a similarity value based on a Bayesian setting. In our framework, distance comparisons are modeled based on distribution functions on the difference vectors. To combine these functions, a similarity score is computed by an Ensemble of weak Bayesian learners for each dimension in the feature space. To find independent dimensions of maximum meaning, we apply a space transformation based on eigenvalue decomposition. In our experiments, we demonstrate that our new method shows promising results compared to related Mahalanobis learners on several test data sets w.r.t. nearest-neighbor classification and precision-recall-graphs.