Search Results for author:
Found 5 papers, 0 papers with code
Calibrated Reliable Regression using Maximum Mean Discrepancy
Accurate quantification of uncertainty is crucial for real-world applications of machine learning.
Dynamic Window-level Granger Causality of Multi-channel Time Series
In this paper, we present the dynamic window-level Granger causality method (DWGC) for multi-channel time series data.
Research on Clustering Performance of Sparse Subspace Clustering
Recently, sparse subspace clustering has been a valid tool to deal with high-dimensional data.
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering
Based on further studying the low-rank subspace clustering (LRSC) and L2-graph subspace clustering algorithms, we propose a F-graph subspace clustering algorithm with a symmetric constraint (FSSC), which constructs a new objective function with a symmetric constraint basing on F-norm, whose the most significant advantage is to obtain a closed-form solution of the coefficient matrix.
Big Learning with Bayesian Methods
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data.
