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Orderly Subspace Clustering
Semi-supervised representation-based subspace clustering is
to partition data into their underlying subspaces by finding
effective data representations with partial supervisions. Essentially, an effective and accurate representation should be
able to uncover and preserve the true data structure. Meanwhile, a reliable and easy-to-obtain supervision is desirable
for practical learning. To meet these two objectives, in this
paper we make the first attempt towards utilizing the orderly relationship, such as the data a is closer to b than to c, as
a novel supervision. We propose an orderly subspace clustering approach with a novel regularization term. OSC enforces the learned representations to simultaneously capture
the intrinsic subspace structure and reveal orderly structure
that is faithful to true data relationship. Experimental results
with several benchmarks have demonstrated that aside from
more accurate clustering against state-of-the-arts, OSC interprets orderly data structure which is beyond what current approaches can offer
Human motion retrieval based on freehand sketch
In this paper, we present an integrated framework of human motion retrieval based on freehand sketch. With some simple rules, the user can acquire a desired motion by sketching several key postures. To retrieve efficiently and accurately by sketch, the 3D postures are projected onto several 2D planes. The limb direction feature is proposed to represent the input sketch and the projected-postures. Furthermore, a novel index structure based on k-d tree is constructed to index the motions in the database, which speeds up the retrieval process. With our posture-by-posture retrieval algorithm, a continuous motion can be got directly or generated by using a pre-computed graph structure. What's more, our system provides an intuitive user interface. The experimental results demonstrate the effectiveness of our method. © 2014 John Wiley & Sons, Ltd
GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction
In voxel-based neuroimage analysis, lesion features have been the main focus
in disease prediction due to their interpretability with respect to the related
diseases. However, we observe that there exists another type of features
introduced during the preprocessing steps and we call them "\textbf{Procedural
Bias}". Besides, such bias can be leveraged to improve classification accuracy.
Nevertheless, most existing models suffer from either under-fit without
considering procedural bias or poor interpretability without differentiating
such bias from lesion ones. In this paper, a novel dual-task algorithm namely
\emph{GSplit LBI} is proposed to resolve this problem. By introducing an
augmented variable enforced to be structural sparsity with a variable splitting
term, the estimators for prediction and selecting lesion features can be
optimized separately and mutually monitored by each other following an
iterative scheme. Empirical experiments have been evaluated on the Alzheimer's
Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of
proposed model is verified by improved stability of selected lesion features
and better classification results.Comment: Conditional Accepted by Miccai,201
In-Out Intermittency in Gap Junction-Coupled Class I^* Neurons
In a series of papers, we have proposed a dynamical model for gap junction-coupled networks of class I^* neurons, and investigated its dynamic characters. We found various dynamic states in a model neural network with diffusively coupled class I¤ neuron models, called μ-models. Among others, hierarchies of intermittent transitions attracted attention in relation with real brain dynamics. This paper is devoted to report a mechanism of the first transition appeared in the intermittenly transitory dynamics among an all-synchronized state, various metachronal waves and a weakly chaotic state. We clarify that this intermittent transition is described as an in-out intermittency
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