Locality sensitive semi-supervised feature selection

Zhao, Jiachang; Lü, Ke; He, Xiaofei

doi:10.1016/j.neucom.2007.06.014

Cited by 174 publications

(86 citation statements)

References 20 publications

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“…In this paper, we use Laplacian regularized least square (LapRLS) [12], which is a state-of-theart semisupervised learning algorithm. LapRLS is formulated on the graph assumption, which has become popular in recent literature of manifold learning [13]- [17]. Specifically, it assumes that when two data points are close, their corresponding labels should also be similar.…”

Section: Machine Learning-based Methods Formentioning

confidence: 99%

Image Compression by Learning to Minimize the Total Error

Zhang

2013

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-In this paper, we consider the problem of lossy image compression. Recently, machine learning techniques have been introduced as effective mechanisms for image compression. The compression involves storing only the grayscale image and a few carefully selected color pixel seeds. For decompression, regression models are learned with the stored data to predict the missing colors. This reduces image compression to standard active learning and semisupervised learning problems. In this paper, we propose a novel algorithm that makes use of all the colors (instead of only the colors of the selected seeds) available during the encoding stage. By minimizing the total color prediction error, our method can achieve a better compression ratio and better colorization quality than previous methods. The experimental results demonstrate the effectiveness of our proposed algorithm.Index Terms-Active learning, image compression, semisupervised learning.

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Section: Machine Learning-based Methods Formentioning

confidence: 99%

Image Compression by Learning to Minimize the Total Error

Zhang

2013

IEEE Trans. Circuits Syst. Video Technol.

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“…denote label cardinality, label density and the number of distinct label combinations, respectively. [7], [16], [23], over popular multi-label datasets. MIFS is chosen because it can capture label correlations as READER does.…”

Section: Experiments Settingsmentioning

confidence: 99%

READER: Robust Semi-Supervised Multi-Label Dimension Reduction

Sun

Kudo

Kimura

2017

IEICE Trans. Inf. & Syst.

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SUMMARYMulti-label classification is an appealing and challenging supervised learning problem, where multiple labels, rather than a single label, are associated with an unseen test instance. To remove possible noises in labels and features of high-dimensionality, multi-label dimension reduction has attracted more and more attentions in recent years. The existing methods usually suffer from several problems, such as ignoring label outliers and label correlations. In addition, most of them emphasize on conducting dimension reduction in an unsupervised or supervised way, therefore, unable to utilize the label information or a large amount of unlabeled data to improve the performance. In order to cope with these problems, we propose a novel method termed Robust sEmi-supervised multi-lAbel DimEnsion Reduction, shortly READER. From the viewpoint of empirical risk minimization, READER selects most discriminative features for all the labels in a semi-supervised way. Specifically, the 2,1 -norm induced loss function and regularization term make READER robust to the outliers in the data points. READER finds a feature subspace so as to keep originally neighbor instances close and embeds labels into a low-dimensional latent space nonlinearly. To optimize the objective function, an efficient algorithm is developed with convergence property. Extensive empirical studies on real-world datasets demonstrate the superior performance of the proposed method.

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“…Like in supervised and unsupervised FS, these methods can be divided into three categories, depending on how they interact with the learning algorithm: filter, wrapper and embedded approaches. Filter methods discover the relevant and redundant features through analyzing the correlation and dependence among features without involving any learning algorithms [10], [11]. The most common filter strategies are based on feature ranking.…”

Section: A Semi-supervised Feature Selectionmentioning

confidence: 99%

“…Clearly, the combination of both paradigms (supervised and unsupervised) allows the merging of sophisticated semi-supervised approaches that can handle both labeled and unlabeled data. The problem of semi-supervised feature selection has attracted a great deal of interest recently and its effectiveness has already been demonstrated in many applications [8], [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Feature Importance Evaluation with Ensemble Learning

Barkia

Elghazel

Aussem

2011

2011 IEEE 11th International Conference on Data Mining

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Abstract-We consider the problem of using a large amount of unlabeled data to improve the efficiency of feature selection in high dimensional datasets, when only a small set of labeled examples is available. We propose a new semi-supervised feature importance evaluation method (SSFI for short), that combines ideas from co-training and random forests with a new permutation-based out-of-bag feature importance measure. We provide empirical results on several benchmark datasets indicating that SSFI can lead to significant improvement over state-of-the-art semi-supervised and supervised algorithms.

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Locality sensitive semi-supervised feature selection

Cited by 174 publications

References 20 publications

Image Compression by Learning to Minimize the Total Error

Image Compression by Learning to Minimize the Total Error

READER: Robust Semi-Supervised Multi-Label Dimension Reduction

Semi-supervised Feature Importance Evaluation with Ensemble Learning

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