71 citations · 80 across the 5 of their papers we have counts for
5 papers
Probabilistic Dimensionality Reduction via Structure Learning
Li Wang
We propose a novel probabilistic dimensionality reduction framework that can naturally integrate the generative model and the locality information of data. Based on this framework,…
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation
Qi Mao, Li Wang, Ivor W. Tsang +1
Many scientific datasets are of high dimension, and the analysis usually requires visual manipulation by retaining the most important structures of data. Principal curve is a widel…
A Split-Merge Framework for Comparing Clusterings
Qiaoliang Xiang, Qi Mao, Kian Ming Chai +3
Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the…
Parameter-Free Spectral Kernel Learning
Qi Mao, Ivor W. Tsang
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing attention in machine learning. In this paper, we propose a novel semi-supervise…
A Feature Selection Method for Multivariate Performance Measures
Qi Mao, Ivor W. Tsang
Feature selection with specific multivariate performance measures is the key to the success of many applications, such as image retrieval and text classification. The existing feat…