Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Hierarchical Discriminant Analysis for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding color and shape patterns in images
Finding color and shape patterns in images
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Fast nonnegative matrix factorization and its application for protein fold recognition
EURASIP Journal on Applied Signal Processing
Survey of Distance Measures for NMF-Based Face Recognition
Computational Intelligence and Security
Topic model methods for automatically identifying out-of-scope resources
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Robust automatic data decomposition using a modified sparse NMF
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Class-specific discriminant non-negative matrix factorization for frontal face verification
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Nonnegative matrix factorizations performing object detection and localization
Applied Computational Intelligence and Soft Computing
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Non-negative matrix factorization (NMF) is an unsupervised algorithm that presents the ability of learning "parts" from visual data. The goal of this technique is to find basis functions such that training examples can be faithfully reconstructed using appropriate combinations of the discovered basis functions. Bases are restricted to non-negative values, and original data is represented by additive combinations of the basis vectors. The space defined by NMF basis lacks of a suitable metric. The aim of this paper is to explore different distance metrics for NMF in the context of statistical classification of objects, and to compare these results to those obtained with a related algorithm: principal component analysis (PCA). We evaluate Earth mover's distance as a relevant metric that takes into account the positive definition of the NMF bases, and it presents the best recognition rates when the dimensionality of data is correctly estimated. We also show that NMF outperforms PCA-based representation when visual data can be partially occluded.