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20142021
most citedSemi-supervised Feature Analysis by Mining Correlations among Multiple Tasks

242 citations · 697 across the 11 of their papers we have counts for

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Showing 2014Show all

7 papers · 1 filter

cs.LG2014★ 1 cited

A Convex Formulation for Spectral Shrunk Clustering

Xiaojun Chang, Feiping Nie, Zhigang Ma +2

Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reducti…

cs.LG2014★ 17 cited

Balanced k-Means and Min-Cut Clustering

Xiaojun Chang, Feiping Nie, Zhigang Ma +1

Clustering is an effective technique in data mining to generate groups that are the matter of interest. Among various clustering approaches, the family of k-means algorithms and mi…

cs.LG2014★ 104 cited

A Convex Sparse PCA for Feature Analysis

Xiaojun Chang, Feiping Nie, Yi Yang +1

Principal component analysis (PCA) has been widely applied to dimensionality reduction and data pre-processing for different applications in engineering, biology and social science…

cs.CV2014★ 29 cited

A Discriminative CNN Video Representation for Event Detection

Zhongwen Xu, Yi Yang, Alexander G. Hauptmann

In this paper, we propose a discriminative video representation for event detection over a large scale video dataset when only limited hardware resources are available. The focus o…

cs.LG2014★ 4 cited

Improved Spectral Clustering via Embedded Label Propagation

Xiaojun Chang, Feiping Nie, Yi Yang +1

Spectral clustering is a key research topic in the field of machine learning and data mining. Most of the existing spectral clustering algorithms are built upon Gaussian Laplacian…

cs.LG2014★ 242 cited

Semi-supervised Feature Analysis by Mining Correlations among Multiple Tasks

Xiaojun Chang, Yi Yang

In this paper, we propose a novel semi-supervised feature selection framework by mining correlations among multiple tasks and apply it to different multimedia applications. Instead…

cs.LG2014★ 219 cited

Compound Rank-k Projections for Bilinear Analysis

Xiaojun Chang, Feiping Nie, Sen Wang +3

In many real-world applications, data are represented by matrices or high-order tensors. Despite the promising performance, the existing two-dimensional discriminant analysis algor…