ZHANG Xiaowei

Transduction on Directed Graphs via Absorbing Random Walks

In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications.Different from existing research efforts that either only deal with undirected graphs or circumvent directionality by means of symmetrization, we propose a novel random walk approach on directed graphs using absorbing Markov chains, which can be regarded as maximizing the accumulated expected number of visits from the unlabeled transient states.

type: 
Journal Paper
journal: 
IEEE Transactions on Pattern Analysis and Machine Intelligence, Issue 99, 2017, doi:10.1109/TPAMI.2017.2730871
pubmed: 
28809671
Url: 
http://ieeexplore.ieee.org/document/8008851/
Impact Factor: 
8.329

Segment 2D and 3D Filaments by Learning Sructured and Contextual Features

We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this

type: 
Journal Paper
journal: 
IEEE Transactions on Medical Imaging, Issue 99, 2016, doi: 10.1109/TMI.2016.2623357
Impact Factor: 
2.536

Incremental Regularized Least Squares for Dimensionality Reduction of LargeScale Data

Over the past few decades, much attention has been drawn to large-scale incremental data analysis, where researchers are faced with huge amount of highdimensional data acquired incrementally. In such a case, conventional algorithms that compute the result from scratch whenever a new sample comes are highly inef-

type: 
Conference Paper/Poster
journal: 
SIAM Journal on Scientific Computing 2016
Impact Factor: 
1.850

Robust Multivariate Regression with Grossly Corrupted Observations and Its Application to Personality Prediction

We consider the multiple-response regression problem, where the response is subject to sparse gross errors, in the high-dimensional setup. We propose a tractable regularized M-estimator that is robust to such error, where the sum of two individual regularization terms are used: the first one encourages row-sparse regression parameters, and the second one encourages a sparse error term. We obtain non-asymptotical estimation error bounds of the proposed method. To the best of our knowledge, this is the first analysis of the robust multi-task regression problem with gross errors.

type: 
Conference Paper/Poster
journal: 
Journal of Machine Learning Research (Workshop and Conference Proceedings) 30:1-15, 2015

A Graph-theoretical Approach for Tracing Filamentary Structures in Neuronal and Retinal Images

The aim of this study is about tracing filamentary structures in both neuronal and retinal images. It is often crucial to identify single neurons in neuronal networks, or separate vessel tree structures in retinal blood vessel networks, in applications such as drug screening for neurological disorders or computeraided diagnosis of diabetic retinopathy. Both tasks are challenging as the same bottleneck issue of filament crossovers is commonly encountered, which essentially hinders the ability of existing systems to conduct large-scale drug screening or practical clinical usage.

type: 
Journal Paper
journal: 
IEEE Transactions on Medical Imaging, DOI 10.1109/TMI.2015.2465962, vol.35, pp.1-17, 2015
pubmed: 
26316029
Url: 
http://www.ncbi.nlm.nih.gov/pubmed/26316029
Impact Factor: 
3.39

Estimate Hand Poses Efficiently from Single Depth Images

This paper aims to tackle the practically very challenging problem of efficient and accurate hand pose estimation from single depth images. A dedicated two-step regression forest pipeline is proposed: Given an input hand depth image, step one involves mainly estimation of 3D location and in-plane rotation of the hand using a pixel-wise regression forest. This is utilized in step two which delivers final hand estimation by a similar regression forest model based on the entire hand image patch. Moreover, our estimation is guided by internally executing a 3D hand kinematic chain model.

type: 
Journal Paper
journal: 
International Journal of Computer Vision (IJCV), Vol. 112, No. 3, May 2015, pp.1-25
Impact Factor: 
3.81
Date of acceptance: 
2015-04-03
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