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Chapter and Conference Paper
Graduated Consistency-Regularized Optimization for Multi-graph Matching
Graph matching has a wide spectrum of computer vision applications such as finding feature point correspondences across images. The problem of graph matching is generally NP-hard, so most existing work pursues...
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Chapter and Conference Paper
On the Convergence of Graph Matching: Graduated Assignment Revisited
We focus on the problem of graph matching that is fundamental in computer vision and machine learning. Many state-of-the-arts frequently formulate it as integer quadratic programming, which incorporates both u...
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Chapter and Conference Paper
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction
Existing feature extraction methods explore either global statistical or local geometric information underlying the data. In this paper, we propose a general framework to learn features that account for both t...
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Chapter and Conference Paper
Optimizing Surface Triangulation Via Near Isometry with Reference Meshes
Optimization of the mesh quality of surface triangulation is critical for advanced numerical simulations and is challenging under the constraints of error minimization and density control. We derive a new meth...
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Chapter and Conference Paper
IKNN: Informative K-Nearest Neighbor Pattern Classification
The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of K depends upon the data, making it laborious to tun...
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Article
Open AccessTowards Inferring Protein Interactions: Challenges and Solutions
Discovering interacting proteins has been an essential part of functional genomics. However, existing experimental techniques only uncover a small portion of any interactome. Furthermore, these data often have...
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Chapter and Conference Paper
Spectral Clustering for Robust Motion Segmentation
In this paper, we propose a robust motion segmentation method using the techniques of matrix factorization and subspace separation. We first show that the shape interaction matrix can be derived using QR decompos...
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Chapter and Conference Paper
Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*
We demonstrate that data clustering amounts to a dynamic process of self-aggregation in which data objects move towards each other to form clusters, revealing the inherent pattern of similarity. Selfaggregation i...
