11,870 research outputs found
Structured Learning of Tree Potentials in CRF for Image Segmentation
We propose a new approach to image segmentation, which exploits the
advantages of both conditional random fields (CRFs) and decision trees. In the
literature, the potential functions of CRFs are mostly defined as a linear
combination of some pre-defined parametric models, and then methods like
structured support vector machines (SSVMs) are applied to learn those linear
coefficients. We instead formulate the unary and pairwise potentials as
nonparametric forests---ensembles of decision trees, and learn the ensemble
parameters and the trees in a unified optimization problem within the
large-margin framework. In this fashion, we easily achieve nonlinear learning
of potential functions on both unary and pairwise terms in CRFs. Moreover, we
learn class-wise decision trees for each object that appears in the image. Due
to the rich structure and flexibility of decision trees, our approach is
powerful in modelling complex data likelihoods and label relationships. The
resulting optimization problem is very challenging because it can have
exponentially many variables and constraints. We show that this challenging
optimization can be efficiently solved by combining a modified column
generation and cutting-planes techniques. Experimental results on both binary
(Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC
2012) segmentation datasets demonstrate the power of the learned nonlinear
nonparametric potentials.Comment: 10 pages. Appearing in IEEE Transactions on Neural Networks and
Learning System
ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space
Studying the function of proteins is important for understanding the
molecular mechanisms of life. The number of publicly available protein
structures has increasingly become extremely large. Still, the determination of
the function of a protein structure remains a difficult, costly, and time
consuming task. The difficulties are often due to the essential role of spatial
and topological structures in the determination of protein functions in living
cells. In this paper, we propose ProtNN, a novel approach for protein function
prediction. Given an unannotated protein structure and a set of annotated
proteins, ProtNN finds the nearest neighbor annotated structures based on
protein-graph pairwise similarities. Given a query protein, ProtNN finds the
nearest neighbor reference proteins based on a graph representation model and a
pairwise similarity between vector embedding of both query and reference
protein-graphs in structural and topological spaces. ProtNN assigns to the
query protein the function with the highest number of votes across the set of k
nearest neighbor reference proteins, where k is a user-defined parameter.
Experimental evaluation demonstrates that ProtNN is able to accurately classify
several datasets in an extremely fast runtime compared to state-of-the-art
approaches. We further show that ProtNN is able to scale up to a whole PDB
dataset in a single-process mode with no parallelization, with a gain of
thousands order of magnitude of runtime compared to state-of-the-art
approaches
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