218 research outputs found
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
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
Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representation
Nonnegative Tucker Factorization (NTF) minimizes the euclidean distance or
Kullback-Leibler divergence between the original data and its low-rank
approximation which often suffers from grossly corruptions or outliers and the
neglect of manifold structures of data. In particular, NTF suffers from
rotational ambiguity, whose solutions with and without rotation transformations
are equally in the sense of yielding the maximum likelihood. In this paper, we
propose three Robust Manifold NTF algorithms to handle outliers by
incorporating structural knowledge about the outliers. They first applies a
half-quadratic optimization algorithm to transform the problem into a general
weighted NTF where the weights are influenced by the outliers. Then, we
introduce the correntropy induced metric, Huber function and Cauchy function
for weights respectively, to handle the outliers. Finally, we introduce a
manifold regularization to overcome the rotational ambiguity of NTF. We have
compared the proposed method with a number of representative references
covering major branches of NTF on a variety of real-world image databases.
Experimental results illustrate the effectiveness of the proposed method under
two evaluation metrics (accuracy and nmi)
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