26 citations · 71 across the 10 of their papers we have counts for
10 papers
A Minimax Probability Machine for Non-Decomposable Performance Measures
Junru Luo, Hong Qiao, Bo Zhang
Imbalanced classification tasks are widespread in many real-world applications. For such classification tasks, in comparison with the accuracy rate, it is usually much more appropr…
Learning with Smooth Hinge Losses
JunRu Luo, Hong Qiao, Bo Zhang
Due to the non-smoothness of the Hinge loss in SVM, it is difficult to obtain a faster convergence rate with modern optimization algorithms. In this paper, we introduce two smooth…
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
Fengfu Li, Hong Qiao, Bo Zhang +1
Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separ…
Effective Deterministic Initialization for -Means-Like Methods via Local Density Peaks Searching
Fengfu Li, Hong Qiao, Bo Zhang
The -means clustering algorithm is popular but has the following main drawbacks: 1) the number of clusters, , needs to be provided by the user in advance, 2) it can easily re…
A New Manifold Distance Measure for Visual Object Categorization
Fengfu Li, Xiayuan Huang, Hong Qiao +1
Manifold distances are very effective tools for visual object recognition. However, most of the traditional manifold distances between images are based on the pixel-level compariso…
Categorification of quantum symmetric pairs I
Huanchen Bao, Peng Shan, Weiqiang Wang +1
We categorify a coideal subalgebra of the quantum group of by introducing a -category à la Khovanov-Lauda-Rouquier, and show that self-dual indecomposable…
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity
Peijie Yin, Hong Qiao, Wei Wu +4
Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor…
Intrinsic dimension estimation of data by principal component analysis
Mingyu Fan, Nannan Gu, Hong Qiao +1
Estimating intrinsic dimensionality of data is a classic problem in pattern recognition and statistics. Principal Component Analysis (PCA) is a powerful tool in discovering dimensi…
An Explicit Nonlinear Mapping for Manifold Learning
Hong Qiao, Peng Zhang, Di Wang +1
Manifold learning is a hot research topic in the field of computer science and has many applications in the real world. A main drawback of manifold learning methods is, however, th…
Isometric Multi-Manifolds Learning
Mingyu Fan, Hong Qiao, Bo Zhang
Isometric feature mapping (Isomap) is a promising manifold learning method. However, Isomap fails to work on data which distribute on clusters in a single manifold or manifolds. Ma…