169 research outputs found
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Non-negative matrix factorization (NMF) has proved effective in many
clustering and classification tasks. The classic ways to measure the errors
between the original and the reconstructed matrix are distance or
Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly
handled when we use these error measures. As a consequence, alternative
measures based on nonlinear kernels, such as correntropy, are proposed.
However, the current correntropy-based NMF only targets on the low-level
features without considering the intrinsic geometrical distribution of data. In
this paper, we propose a new NMF algorithm that preserves local invariance by
adding graph regularization into the process of max-correntropy-based matrix
factorization. Meanwhile, each feature can learn corresponding kernel from the
data. The experiment results of Caltech101 and Caltech256 show the benefits of
such combination against other NMF algorithms for the unsupervised image
clustering
Document Clustering Based On Max-Correntropy Non-Negative Matrix Factorization
Nonnegative matrix factorization (NMF) has been successfully applied to many
areas for classification and clustering. Commonly-used NMF algorithms mainly
target on minimizing the distance or Kullback-Leibler (KL) divergence,
which may not be suitable for nonlinear case. In this paper, we propose a new
decomposition method by maximizing the correntropy between the original and the
product of two low-rank matrices for document clustering. This method also
allows us to learn the new basis vectors of the semantic feature space from the
data. To our knowledge, we haven't seen any work has been done by maximizing
correntropy in NMF to cluster high dimensional document data. Our experiment
results show the supremacy of our proposed method over other variants of NMF
algorithm on Reuters21578 and TDT2 databasets.Comment: International Conference of Machine Learning and Cybernetics (ICMLC)
201
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals
The Partial Least Square Regression (PLSR) exhibits admirable competence for
predicting continuous variables from inter-correlated brain recordings in the
brain-computer interface. However, PLSR is in essence formulated based on the
least square criterion, thus, being non-robust with respect to noises. The aim
of this study is to propose a new robust implementation for PLSR. To this end,
the maximum correntropy criterion (MCC) is used to propose a new robust variant
of PLSR, called as Partial Maximum Correntropy Regression (PMCR). The
half-quadratic optimization is utilized to calculate the robust projectors for
the dimensionality reduction, and the regression coefficients are optimized by
a fixed-point approach. We evaluate the proposed PMCR with a synthetic example
and the public Neurotycho electrocorticography (ECoG) datasets. The extensive
experimental results demonstrate that, the proposed PMCR can achieve better
prediction performance than the conventional PLSR and existing variants with
three different performance indicators in high-dimensional and noisy regression
tasks. PMCR can suppress the performance degradation caused by the adverse
noise, ameliorating the decoding robustness of the brain-computer interface
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