Semi-Supervised Discriminant Analysis Based On Data Structure

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. VII (May – Jun. 2015), PP 39-46
www.iosrjournals.org
DOI: 10.9790/0661-17373946 www.iosrjournals.org 39 | Page
Semi-Supervised Discriminant Analysis Based On Data Structure
Xuesong Yin*
, Rongrong Jiang, Lifei Jiang
Department of Computer Science & Technology, Zhejiang Radio & TV University, Hangzhou, 310030, China
Abstract: Dimensionality reduction is a key data-analytic technique for mining high-dimensional data. In this
paper, we consider a general problem of learning from pairwise constraints in the form of must-link and cannot-
link. As one kind of side information, the must-link constraints imply that a pair of instances belongs to the same
class, while the cannot-link constraints compel them to be different classes. Given must-link and cannot-link
information, the goal of this paper is learn a smooth and discriminative subspace. Specifically, in order to
generate such a subspace, we use pairwise constraints to present an optimization problem, in which a least
squares formulation that integrates both global and local structures is considered as a regularization term for
dimensionality reduction. Experimental results on benchmark data sets show the effectiveness of the proposed
algorithm.
Keywords: Dimensionality reduction; Pairwise constraints; High-dimensional data; Discriminant analysis.
I. Introduction
Applications in various domains such as face recognition, web mining and image retrieval often lead to
very high-dimensional data. Mining and understanding such high-dimensional data is a contemporary challenge
due to the curse of dimensionality. However, the current researches [3,4,5,6,13] show that reducing the
dimensionality of data has been a feasible method for more precisely charactering such data. Therefore,
dimensionality reduction (DR for short) makes sense in many practical problems [2,3,5] since it allows one to
represent the data in a lower dimensional space. Based on the availability of the side information,
dimensionality reduction algorithms fall into three categories: supervised DR [7,12,28,35], semi-supervised DR
[8,16,18,21,22] and unsupervised DR [10,13,14,15,34]. In this paper, we focus on the case of semi-supervised
DR. With few constraints or class label information, existing semi-supervised DR algorithms appeal to
projecting the observed data onto a low-dimensional manifold, where the margin between data form different
classes is maximized. Most algorithms in this category, such as Locality Sensitive Discriminant Analysis [20],
Locality Sensitive Semi-supervised Feature Selection [36], and Semi-supervised Discriminant Analysis [26],
greatly take the intrinsic geometric structure into account, i.e. local structure of the data, indicating that nearby
instances are likely to have the same label. However, global structure of the data, implying that instances on the
same structure (typically referred to as a cluster or a manifold) are like to have the same label [1], is ignored in
those algorithms. In fact, when mining the data, global structure is the same significance as local structure
[1,10,11,29]. Therefore, it is necessary to integrate both global and local structures into the process of DR. The
key challenge is how we can incorporate relatively few pairwise constraints into DR such that the represented
data can still capture the available class information.
To this end, we propose a semi-supervised discriminant analysis algorithm with integrating both global
and local structures (SGLS), which naturally address DR under semi-supervised settings. Given pairwise
constraints, the key idea in SGLS is to integrate both global and local structures in semi-supervised discriminant
framework so that both discriminant and geometric structures of the data can be accurately captured. The SGLS
algorithm has the same flavor as not only supervised DR algorithms, which try to adjust the distance among
instances to improve the separability of the data, but unsupervised DR algorithms as well, which make nearby
instances in the original space close to each other in the embedding space.
The remainder of the paper is organized as follows. In Section 2, related work on the existing
algorithms of semi-supervised DR is discussed. In Section 3, we introduce the general framework of our
proposed SGLS algorithm. Section 4 discusses the experimental results on a number of real-world data sets.
Finally, we draw conclusions in Section 5.
II. Related Work
Unsupervised DR has attracted considerable interest in the last decade [9,19,23,25,29]. Recently, a few
researchers [5,9,13,17] have considered the case that discovers the local geometrical structure of the data
manifold when the data lie in a low-dimensional space of the high-dimensional ambient space. Algorithms
exploited along this line are used to estimate geometrical and topological properties of the embedding space,
*Corresponding author: Xuesong Yin, yinxs@nuaa.edu.cn

Semi-supervised Discriminant Analysis based on Data Structure
such as Locality Preserving Projections (LPP) [34], Local Discriminant Embedding (LDE) [35], Marginal Fisher
Analysis (MFA) [37] and Transductive Component Analysis (TCA) [27]. Different with typical unsupervised
DR algorithms, more lately, K-means was applied in the dimension-reduced space for avoiding the curse of
dimensionality in several algorithms [25,31] which perform clustering and dimensionality reduction
simultaneously.
Linear Discriminant Analysis (LDA) [7,12], capturing the global geometric structure of the data by
simultaneously maximizing the between-class distance and minimizing the within-class distance, is a well-
known approach for supervised DR. It has been used widely in various applications, such as face recognition
[5]. However, a major shortcoming of LDA is that it fails to discover the local geometrical structure of the data
manifold.
Semi-supervised DR can be considered as a new issue in DR area, which learns from a combination of
both the side information and unlabeled data. In general, the side information can be expressed in diverse forms,
such as class labels and pairwise constraints. Pairwise constraints include both must-link form in which the
instances belong to the same class and cannot-link form in which the instances belong to different classes. Some
semi-supervised DR algorithms [20,26,27,30,36,37] just applied partly labeled instances to maximize the margin
between instances form different classes for improving performance. However, in many practical data mining
applications, it is fairly expensive to label instances. In contrast, it could be easier for an expert or a user to
specify whether some pairs of instances belong to the same class or not. Moreover, the pairwise constraints can
be derived from labeled data but not vice versa. Therefore, making use of pairwise constraints to reduce the
dimensionality of the data has been an important topic [8,16,33].
III. The SGLS Algorithm
3.1 The Integrating Global and Local structures Framework
Given a set of instances 1, , nx x in
N
R , we can use a k-nearest neighbor graph to model the relationship
between nearby data points. Specifically, we put an edge between nodes i and j if ix and jx are close, i.e. ix and
jx are among k nearest neighbors of each other. Let the corresponding weight matrix be S, defined by
2
2
exp( ) ( ) ( )
0
i j
i k j j k i
ij
x x
x N x or x N xS t
otherwise
 
    


(1)
Where ( )k iN x denotes the set of k nearest neighbors of ix . In general, if a weighted value ijS is large, ix and jx
are close to each other in the original space. Thus, in order to preserve the locality structure, the embedding
space obtained should be as smooth as possible. To learn an appropriate representation y ( 1[ , , ]T
ny y y  )
with the locality structure, it is common to minimize the following objective function [13,34]:
2
,
2 T
i j ij
i j
y y S y Ly  (2)
where D is a diagonal matrix whose diagonal entries are column (or row, since S is symmetric) sum of S, that is
1
n
ii ijj
D S
  . L D S  is the Laplacian matrix.
On the other hand, we expect that the projections{ }T
ia x generated by a projection vector
d
a R , which span
the final embedding space, approach the sufficiently smooth embeddings{ }iy as much as possible. This
expectation can be satisfied by the least squares formulation which can incorporate the local structure
information via a regularization term defined as in Eq. (2). Mathematically, a projection vector a can be acquired
by solving the following optimization problem [27]:
2
1min ( , ) T T
J a y X a y y Ly   (3)
where 1 0  is the trade-off parameter. Here we assume that the data matrix X has been centered.
Taking the derivative of J(a,y) with respect to y and setting it equals to zero, we get

1
( , )
2( ) 0TJ a y
y X a Ly
y


   

(4)
It follows that
1
1* ( ) T
y I L X a 
  (5)
where I is the identity matrix of size n.
Applying Eq. (5) to Eq. (3), we can eliminate y and obtain the following optimization problem with regard to
a:
1 1
1
1 1
min ( ) ( , *) * ( )( ) *
( ) ( )
T
T T
T T
J a J a y y L I L y
a X I L L X a
a XGX a
 
 
  
 

(6)
where G is a positive semi-definite matrix of size n by n and has the following expression:
1
1 1( ) ( )G I L L 
  (7)
We can observe from Eq. (6) that minimizing J will bring the smooth subspace where both global and local
structures of data are calculated with regard to a.
3.2 Discriminat Analysis With Pairwise Constraints
Suppose we are given two sets of pairwise constraints including must-link (M) and cannot-link (C). In
the previous work [32,38,40,41], such constraints were used for learning an adaptive metric between the
prototypes of instances. However, recent research has shown that the distance metric learned on high-
dimensional space is relatively unreliable [33,39]. Instead of using constraint-guided metric learning, in this
paper our goal is to use pairwise constraints to find a projection vector such that in the embedding space the
distance between any pair of instances involved in the cannot-link constraints is maximized while that between
any pair of instances involved must-link constraints is minimized.
If the projection vector a is acquired, we can introduce such a transformation:
T
i iy a x (8)
Under this transformation, we calculate the following sum of the squared distances of the point pairs in M:
( , )
( ) ( )
i j
T T T T T
w i j i j
x x M
T
w
d a x a x a x a x
a S a

  


(9)
where wS is the covariance matrix of the point pairs in M:
( , )
( )( )
i j
T
w i j i j
x x M
S x x x x

   (10)
Correspondingly, for the point pairs in C, we have
T
b bd a S a (11)
where bS has the following expression form:
( , )
( )( )
i j
T
b i j i j
x x C
S x x x x

   (12)
Similar to LDA that seeks directions on which the data points of different classes are far from each other
while requiring data points of the same class to be close to each other, we try to maximize bd and minimize wd .
Thus, we can optimize the following problem to obtain the projection direction:
max
T
b
Ta
w
a S a
a S a
(13)

3.3 The Optimization Framework
So far, we pursue the projection direction a from two formulations: (1) we introduce a least squares
formulation for finding a, which facilities the integration of global and local structures; (2) we obtain the
projection vector by maximizing the cannot-link covariance and simultaneously minimizing the must-link
covariance. When the first formulation is considered as a regularization term, we arrive at the following
optimization problem:
2 2
max
(1 ) ( )
T
b
Ta
w
a S a
a S a J a  
(14)
where 20 1  controls the smoothness of estimator. The optimal projection vector a that maximizes the
objective function (14) is acquired by the maximum eigenvalue solution to the following generalized eigenvalue
problem.
2 2((1 ) )T
b wS a S XGX a     (15)
In real-world applications, we usually need more projection vectors to span a subspace rather than just one.
Let the matrix charactering such a target subspace be 1[ , , ]dA a a  which is formed by the eigenvectors
associated with the d largest eigenvalues of Eq. (15). Also, it is worth noting that since our method is linear, it
has a direct out-of-sample extension to novel sample x, i.e.
T
A x .
Based on the analysis above, we propose to develop a semi-supervised dimensionality reduction algorithm
which integrates both global and local structures defined in Eq. (14). The corresponding algorithm, called SGLS
is presented in Algorithm 1.
Algorithm 1
Input: X, M, C.
Output: A projection matrix
N d
A R 
 .
1 Construct an unsupervised graph on all n instances to calculate S as in Eq. (2) and L = D – S.
2 Present a least squares formulation together with the local structure information to compute matrix G as in
Eq. (7).
3 Compute wS and bS as in Eqs. (10) and (12).
4 Compute a generalized eigenvalue problem Eq. (15) and let the solved eigenvectors be 1, , da a in an
order of decreasing eigenvalues.
5 Output 1[ , , ]dA a a  .
To get a stable solution of the eigen-problem in Eq. (15), the matrix is required to be non-singular
which is not true when the number of features is larger than the number of instances, i.e. N > n. In such case, we
can apply the Tikhonov regularization technique [24,26] to improve the estimation. Thus, the generalized eigen-
problem in this paper can be rewritten as:
2 2((1 ) )T
b w NS a S XGX I a       (16)
where NI is the identity matrix of size N and 0  is a regularization parameter.
IV. Experiments
In this section, we study the performance of our SGLS algorithm in terms of clustering accuracy on
several real-world data sets from different application domains. We set k = 5 and t = 1 for the construction of the
similarity matrix S defined in Eq. (1).
4.1 Experimental Setup
In order to verify the efficacy of the proposed algorithm, we compare it with the state-of-the-art, semi-
supervised dimensionality reduction algorithms including SSDR [22], SCREEN [33], LADKM [31] and LMDM
[16]. We implemented SGLS in the MATLAB environment. All experiments were conducted on a PENTIUM
DUAL 3G PC with 2.0 GB RAM. We compared all algorithms on seven benchmark data sets, including
Segment and Digits from UCI machine Learning Repository, three document data sets DOC1, DOC2 and DOC3
from the 20-Newgroups data, and two image data sets : USPS handwritten data and Yale Face B (YALEB). The
statistics of all data sets are summarized in Table 1.

Table 1: Summary of the test datasets used in our experiment
Data sets Instance Dimension Class
Segment 2309 19 10
Digits 6300 16 10
DOC1 4000 4985 6
DOC2 5200 3132 5
DOC3 6500 6466 9
USPS 7122 256 10
YALEB 3110 2500 10
As the unified clustering platform, we use K-means algorithm as the clustering method to compare
several algorithms after dimensionality reduction. For each data set, we ran different algorithms for 20 times and
the comparison was based on the average performance. The parameter in SGLS are always set to 1 10  and
2 0.8  if without extra explanation. Moreover, for the fair comparison, the dimensionality of all the
algorithms is set to K-1 (K is the number of clustering).
To evaluate the clustering performance, in this paper, we employ normalized mutual information (NMI) as the
clustering validation measure, which is widely used to verify the performance of clustering algorithms [8,25,33].
Then, NMI is defined as
( , )
( ) ( )
I X Y
NMI
H X H Y
 (17)
where X and Y denote the random variables of cluster memberships from the ground truth and the output of
clustering algorithm, respectively. ( , )I X Y is the mutual information between X and Y. ( )H X and ( )H Y are
the entropies of X and Y respectively.
4.2 Experimental Results

Figure 1 Clustering performance on 6 data sets with different number of constraints
Table 2 presents the NMI results of various algorithms on all seven data sets. Also, Figure 1 shows the
clustering performance of standard K-means applied to the projected data by different dimensionality reduction
algorithms with different numbers of pairwise constraints. As can be seen, our SGLS algorithm achieves the best
performance on Digits, DOC1, DOC3, USPS, and YALEB data sets. In fact, the performance of our algorithm is
always comparable to that of the other algorithm on the rest of all seven data sets used in this paper. Specifically,
we can obtain the following main observations from Table 2 and Figure 1:
(1) The SGLS algorithm significantly outperforms not only LMDM and LDAKM on all of the seven data sets
used in the experiment, but SCREEN and SSDR on the six data sets as well. This can be contributed to such
a fact that integrating both global and local structures in SGLS may be beneficial.
(2) It is interesting to note that SGLS slightly underperform both SSDR on Segment and SCREEN on DOC2.
This implies that in certain case such as Segment, global and local structures may capture similar
information so that integrating such both structures seems to hardly help in the dimensionality reduction
process.
(3) It is clear from the presented results that LDAKM performs relatively worse that the other algorithms. This
can be explained that LDAKM does not employ any side information to pursue the projection direction,
which only makes use of the abundant unlabeled instances. Thus, LDAKM has a greatly inferior
performance.
(4) Due to the fact that SCREEN and LMDM just pairwise constraints to learn an optimal subspace, their
performances are worse that those of SSDR and SGLS respectively. In fact, a large number of the unlabeled
data play an important role for semi-supervised dimensionality reduction [8,26,27].
(5) Since SSDR can apply both the pairwise constraints and the unlabeled data to learn a subspace, it can
provide a relatively satisfying clustering performance in comparison with LDAKM, SCREEN and LMDM.
Compared with our SGLS, however, SSDR has still provided the fairly inferior performance. Actually,
SSDR can be considered as the constrained Principal Component Analysis (PCA). Thus, it fails to preserve
local neighborhood structures of the instances in the reduced low-dimensional space.
Table 2: NMI comparisons on seven data sets
Data sets LDAKM SCREEN SSDR LMDM SGLS
Segment 0.687 0.723 0.744 0.712 0.737
Digits 0.714 0.755 0.767 0.750 0.788
DOC1 0.567 0.596 0.604 0.588 0.625
DOC2 0.502 0.561 0.547 0.539 0.556
DOC3 0.746 0.802 0.810 0.797 0.823
USPS 0.636 0.675 0.682 0.669 0.705
YALEB 0.811 0.848 0.837 0.833 0.861
V. Conclusions
In this paper, we propose a novel semi-supervised dimensionality reduction algorithm, called SGLS.
For dimensionality reduction and clustering, SGLS integrates both global and local geometric structures so that
it can be naturally extended to deal with the abundant labeled data, as graph Laplacian is defined on all the
instances. Compared with the state-of-the-art dimensionality reduction algorithms on a collection of benchmark
data sets in terms of clustering accuracy, SGLS can always achieve a clear performance gain. Extensive
experiments exhibit the advantages of the novel semi-supervised clustering method of SGLS+K-means.

Acknowledgments
The research reported in this paper has been partially supported by Natural Science Foundation of
Zhejiang Province under Grant No. Y1100349, Public Project of Zhejiang Province under Grant No.
2013C33087, Academic Climbing Project of Young Academic Leaders of Zhejiang Province No. pd2013446.
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