International Core Journal of Engineering 2020-26 | Page 122
2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)
Semi-supervised Structured Sparse Graph Data
Classification
Shuai Shao Mingze Tang
University of Electronic Science and Technology of China,
Chengdu, China
[email protected] Beijing University of Posts and Communications,
Beijing, China
[email protected]
classifiers. The classifier design first needs to figure out the
object and how it is represented. In general, multiple feature
vectors are methods used to represent objects. In this way,
the target can be regarded as a point in the multi-dimensional
space, which is very beneficial for calculation and
characterization. For example, when calculating the
similarity or measuring the distance, the space midpoint can
be used, or by using the main Methods such as composition
analysis to reduce feature dimensions. The classification
model based on machine learning has been developed for
many years and there are many tools available [3].
Abstract—Face classification has been developed for many
years with the aim of dividing the face image into the identity
of the person to whom they belong. The existing classification
methods still have some problems such as the classifier is
difficult to conform to the multi-level graph model and the
data in the real scene rarely has a valid label. In this paper, a
semi-supervised structured sparse graph data classification
method for face feature extraction is proposed. The method of
transforming graph data into uniform length vector is used to
construct a semi-supervised method using the manifold that
preserves the global data structure and local data structure.
Sparse graph data classifier was utilized in which sparse
learning automatically obtains the connection relationship
between the label data vector and the unlabeled data vector
and its weight with the L2 norm to control the model. Finally it
realizes the sparse representation of the semi-supervised data.
Experiments based on standard face datasets show that this
semi-supervised sparse graph data classification method has
better classification performance.
Kernel classifiers in machine learning have many
advantages, such as the ability to process noisy data well.
However, the mainstream supervised method in machine
learning, whether the model is effective or not depends
mainly on the number of data and whether the label is good,
and the corresponding unsupervised method of machine
learning has been paid attention to in the field of graph
classification. The advantage of the unsupervised method is
that the performance of the model is not completely
dependent on the quantity of data and the quality of the
annotation. Generally, the feature engineering pre-processes
the data, and the data is first dimensioned and selected by
prior knowledge, although the time consumption is relative
to supervised methods are higher and more dependent on the
scenario [4].
Keywords—Semi-supervised, structured sparse graph, face
classification, manifold, L2 norm
I. I NTRODUCTION
In more and more practical applications, such as
biological sequences, compounds, natural language texts,
and semi-structured data, graphs are proposed and used as a
modeling object. In this modeling approach, the target is
represented as a graph, with a complex structure, rather than
a traditional method of representing the target as a feature
vector. Graph classification is a very important branch of the
graph model and is widely used in the above applications.
The challenge comes from the complex structure of the
graph itself, and the lack of a model that is characterized by
vectors. Machine learning algorithms are widely used as a
primary means of graph classification. In the past machine
learning algorithms, numerical vectors were used instead of
graphs to represent specified targets. In the classification of
graphs, more nuclear methods, such as SVM, are used. The
assumption of using such a kernel method is that there may
be some structural types in the feature vector extracted by the
target. The purpose is to generate a classification hyperplane,
and the extracted target features can be mapped into high-
dimensional space, the problem is that such a kernel function
is difficult to explicitly calculate the correlation and specific
characteristics between structures [1, 2].
The semi-supervised classification problem is used to
continue classification of nodes that are not labeled, and is
used in directed graphs with positive and negative weighting,
such as web page classification and document classification
and biology. The graph nodes in the web page classification
are used to represent the corresponding pages. The
relationship between the pages is represented by the directed
edges. At the same time, the graph connectivity is used when
classifying the nodes without annotations. The prior
knowledge required for this method is the graph. The
mapping of each node's association and classification in the
real scene, the typical a priori assumption is that the edge has
a certain influence on the node category. It includes two
structures: co-links with directed graphs and directed graphs
with the same sub-nodes have been widely used in practical
applications such as web page classification. The classifier
design for the graph node classification problem can adopt
the support vector machine algorithm. The problem is that
this kind of kernel algorithm has no view global and only
pays attention to the relationship between nodes. Further
consideration is given to the global and localized techniques
of directed graphs [5].
The machine learning classification models are derived
from a large number of data training, using machine learning
classification rules in training, Bayesian classification and
support vector machines are typical machine learning
978-1-7281-4691-1/19/$31.00 ©2019 IEEE
DOI 10.1109/AIAM48774.2019.00027
In the graph structure of the graph classifier method, the
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