Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

Li, Zechao; Liu, Jing; Yang, Yi; Zhou, Xiaofang; Lu, Hanqing

doi:10.1109/tkde.2013.65

Cited by 244 publications

(19 citation statements)

References 27 publications

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“…For example, Li et al's robust structured subspace learning (RSSL) [23] uses the 2,1 -norm for sparse feature extraction, combining high-level semantics with low-level, localitypreserving features. In the feature selection algorithm clustering-guided sparse structural learning (CGSSL) by Li et al [24], features are jointly selected using sparse regularization (via the 2,1 -norm) and a non-negative spectral clustering objective. Not only are the selected features sparse; they also are the most discriminative features in terms of predicting the cluster indicators in both the original space and a lower-dimensional subspace on which the data is assumed to lie.…”

Section: Related Workmentioning

confidence: 99%

Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

Weaver

Saito

2018

Computational Intelligence for Pattern Recognition

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Sparse representation-based classification (SRC), proposed by Wright et al., seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class.Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the training set. Our proposed classification algorithm enlarges the training set by using local principal component analysis to approximate the basis vectors of the tangent hyperplane of the class manifold at each training sample. The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors. We use a synthetic data set and three face databases to demonstrate that this method can achieve higher classification accuracy than SRC in cases of sparse sampling, nonlinear class manifolds, and stringent dimension reduction.given training set is insufficient to generalize the data set's class structure) [5], as well as occlusion and noise [2].In 2009, Wright et al. proposed sparse representation-based classification (SRC) [2]. SRC was motivated by the recent boom in the use of sparse representation in signal processing (see, e.g., the work of Candès [6]). The catalyst of these advancements was the discovery that, under certain conditions, the sparsest representation of a signal using an over-complete set of vectors (often called a dictionary) could be found by minimizing the 1 -norm of the representation coefficient vector [7]. Since the 1 -minimization problem is convex, this gave rise to a tractable approach to obtaining the sparsest solution.SRC applies this relationship between the minimum 1 -norm and the sparsest solution to classification.The algorithm seeks the sparsest decomposition of a test sample over the dictionary of training samples via 1minimization, with classification to the class whose corresponding portion of the representation approximates the test sample with least error. The method assumes that class manifolds are linear subspaces, so that the test sample can be represented using training samples in its ground truth class. Wright et al. [2] argue that this is precisely the sparsest decomposition of the test sample over the training set. They make the case that sparsity is critical to high-dimensional image classification and that, if properly harnessed, it can lead to superior classification performance, even on highly corrupted or occluded images. Further, good results can be achieved regardless of the choice of image features that are used for classification, provided that the number of retained features is large enough [2]. Though SRC was originally applied to face recognition, similar methods have been employed in clustering [8], dimension reduction [9], and texture and handwritten digit classification [10].The SRC assumption that class manifolds are linear subspaces is often violated; e.g., facial images that v...

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Section: Related Workmentioning

confidence: 99%

Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

Weaver

Saito

2018

Computational Intelligence for Pattern Recognition

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“…Sparse filtering and EPLS do not attempt to explicitly model the input distribution but focus on the properties of the output distribution instead. In order to remove the redundancies and noise in the high-dimensional features, feature selection technique [36][37] and nonnegative matrix factorzation (NMF) [38] are often adopted to identify a subset of the most useful features.…”

Section: Related Workmentioning

confidence: 99%

A Sparse Target Matrix Generation Based Unsupervised Feature Learning Algorithm for Image Classification

2018

KSII TIIS

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Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm (L 2 ) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.

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“…Zechao Li et al [17] proposed a new unsupervised feature selection algorithm by integrating cluster analysis and sparse structure analysis. In particular, Nonnegative Spectral Clustering is used to learn the label of the input sample more accurately and is also responsible for the function of feature selection.…”

Section: Related Workmentioning

confidence: 99%

Clustering Algorithm for Time Series with Similar Shapes

2018

KSII TIIS

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Since time series clustering is performed without prior information, it is used for exploratory data analysis. In particular, clusters of time series with similar shapes can be used in various fields, such as business, medicine, finance, and communications. However, existing time series clustering algorithms have a problem in that time series with different shapes are included in the clusters. The reason for such a problem is that the existing algorithms do not consider the limitations on the size of the generated clusters, and use a dimension reduction method in which the information loss is large. In this paper, we propose a method to alleviate the disadvantages of existing methods and to find a better quality of cluster containing similarly shaped time series. In the data preprocessing step, we normalize the time series using z-transformation. Then, we use piecewise aggregate approximation (PAA) to reduce the dimension of the time series. In the clustering step, we use density-based spatial clustering of applications with noise (DBSCAN) to create a precluster. We then use a modified K-means algorithm to refine the preclusters containing differently shaped time series into subclusters containing only similarly shaped time series. In our experiments, our method showed better results than the existing method.

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Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

Cited by 244 publications

References 27 publications

Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

A Sparse Target Matrix Generation Based Unsupervised Feature Learning Algorithm for Image Classification

Clustering Algorithm for Time Series with Similar Shapes

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