104 citations · 318 across the 24 of their papers we have counts for
10 papers · 1 filter
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data
Bin Gu, Zhiyuan Dang, Xiang Li +1
In a lot of real-world data mining and machine learning applications, data are provided by multiple providers and each maintains private records of different feature sets about com…
Robust Linear Discriminant Analysis Using Ratio Minimization of L1,2-Norms
Feiping Nie, Hua Wang, Zheng Wang +1
As one of the most popular linear subspace learning methods, the Linear Discriminant Analysis (LDA) method has been widely studied in machine learning community and applied to many…
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng +2
Image clustering is one of the most important computer vision applications, which has been extensively studied in literature. However, current clustering methods mostly suffer from…
A Closed Form Solution to Multi-View Low-Rank Regression
Shuai Zheng, Xiao Cai, Chris Ding +2
Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity…
Clinical Information Extraction via Convolutional Neural Network
Peng Li, Heng Huang
We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pa…
Non-Greedy L21-Norm Maximization for Principal Component Analysis
Feiping Nie, Heng Huang
Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle high-dimensional data. However, due to the high computational complexity of its eigen…
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Wenhao Jiang, Cheng Deng, Wei Liu +3
Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically…
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…
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…
An Iterative Locally Linear Embedding Algorithm
Deguang Kong, Chris H. Q. Ding, Heng Huang +1
Local Linear embedding (LLE) is a popular dimension reduction method. In this paper, we first show LLE with nonnegative constraint is equivalent to the widely used Laplacian embedd…