Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face Recognition with Weighted Locally Linear Embedding
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
A multi-step strategy for approximate similarity search in image databases
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Fuzzy Measures on the Gene Ontology for Gene Product Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Visualization of Non-vectorial Data Using Twin Kernel Embedding
AIDM '06 Proceedings of the International Workshop on on Integrating AI and Data Mining
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel laplacian eigenmaps for visualization of non-vectorial data
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
| Hi-index | 0.00 |
Biometric data like fingerprints are often highly structured and of high dimension. The "curse of dimensionality" poses great challenge to subsequent pattern recognition algorithms including neural networks due to high computational complexity. A common approach is to apply dimensionality reduction (DR) to project the original data onto a lower dimensional space that preserves most of the useful information. Recently, we proposed Twin Kernel Embedding (TKE) that processes structured or non-vectorial data directly without vectorization. Here, we apply this method to clustering and visualizing fingerprints in a 2-dimensional space. It works by learning an optimal kernel in the latent space from a distance metric defined on the input fingerprints instead of a kernel. The outputs are the embeddings of the fingerprints and a kernel Gram matrix in the latent space that can be used in subsequent learning procedures like Support Vector Machine (SVM) for classification or recognition. Experimental results confirmed the usefulness of the proposed method.