4 research outputs found
Application of three graph Laplacian based semi-supervised learning methods to protein function prediction problem
Protein function prediction is the important problem in modern biology. In
this paper, the un-normalized, symmetric normalized, and random walk graph
Laplacian based semi-supervised learning methods will be applied to the
integrated network combined from multiple networks to predict the functions of
all yeast proteins in these multiple networks. These multiple networks are
network created from Pfam domain structure, co-participation in a protein
complex, protein-protein interaction network, genetic interaction network, and
network created from cell cycle gene expression measurements. Multiple networks
are combined with fixed weights instead of using convex optimization to
determine the combination weights due to high time complexity of convex
optimization method. This simple combination method will not affect the
accuracy performance measures of the three semi-supervised learning methods.
Experiment results show that the un-normalized and symmetric normalized graph
Laplacian based methods perform slightly better than random walk graph
Laplacian based method for integrated network. Moreover, the accuracy
performance measures of these three semi-supervised learning methods for
integrated network are much better than the best accuracy performance measures
of these three methods for the individual network.Comment: 16 pages, 9 table
Hypergraph based semi-supervised learning algorithms applied to speech recognition problem: a novel approach
Most network-based speech recognition methods are based on the assumption
that the labels of two adjacent speech samples in the network are likely to be
the same. However, assuming the pairwise relationship between speech samples is
not complete. The information a group of speech samples that show very similar
patterns and tend to have similar labels is missed. The natural way overcoming
the information loss of the above assumption is to represent the feature data
of speech samples as the hypergraph. Thus, in this paper, the three
un-normalized, random walk, and symmetric normalized hypergraph Laplacian based
semi-supervised learning methods applied to hypergraph constructed from the
feature data of speech samples in order to predict the labels of speech samples
are introduced. Experiment results show that the sensitivity performance
measures of these three hypergraph Laplacian based semi-supervised learning
methods are greater than the sensitivity performance measures of the Hidden
Markov Model method (the current state of the art method applied to speech
recognition problem) and graph based semi-supervised learning methods (i.e. the
current state of the art network-based method for classification problems)
applied to network created from the feature data of speech samples.Comment: 11 pages, 1 figure, 2 tables. arXiv admin note: substantial text
overlap with arXiv:1212.038
To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods
To detect the irregular trade behaviors in the stock market is the important
problem in machine learning field. These irregular trade behaviors are
obviously illegal. To detect these irregular trade behaviors in the stock
market, data scientists normally employ the supervised learning techniques. In
this paper, we employ the three graph Laplacian based semi-supervised ranking
methods to solve the irregular trade behavior detection problem. Experimental
results show that that the un-normalized and symmetric normalized graph
Laplacian based semi-supervised ranking methods outperform the random walk
Laplacian based semi-supervised ranking method.Comment: 11 page
Tensor Sparse PCA and Face Recognition: A Novel Approach
Face recognition is the important field in machine learning and pattern
recognition research area. It has a lot of applications in military, finance,
public security, to name a few. In this paper, the combination of the tensor
sparse PCA with the nearest-neighbor method (and with the kernel ridge
regression method) will be proposed and applied to the face dataset.
Experimental results show that the combination of the tensor sparse PCA with
any classification system does not always reach the best accuracy performance
measures. However, the accuracy of the combination of the sparse PCA method and
one specific classification system is always better than the accuracy of the
combination of the PCA method and one specific classification system and is
always better than the accuracy of the classification system itself.Comment: It has some errors in the experimental sectio