Locality preserving score for joint feature weights learning

Yan, Hui; Yang, Jian

doi:10.1016/j.neunet.2015.06.001

Cited by 10 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SPFW is similar as our latest work [28] because both have the same feature search strategy, which learns feature weights jointly in an unsupervised way. However, their feature measurements are different.…”

Section: Basic Idea and Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Sparse preserving feature weights learning

2016

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a novel unsupervised feature selection algorithm, named sparse preserving feature weights learning (SPFW), which is based on the recent local data representation theory, sparse representation. SPFW differs from traditional feature selection algorithms in two aspects: (1) SPFW is designed on the locality measurement criterion with sparse reconstruction residual minimization. It adaptively determines the locality based on sparse representation, instead of fixing the k-nearest neighbors in the original feature space. (2) SPFW selects the most discriminative feature subset from the whole feature set in batch mode, instead of selecting features individually. To optimize the proposed formulation, we propose an efficient iterative algorithm, where each iteration reduces to a subproblem which can be solved with some off-the-shelf toolboxes. We conduct experiments on two face datasets to evaluate the performance of feature selection in terms of classification and clustering, which demonstrate the effectiveness of the proposed algorithm.

show abstract

Section: Basic Idea and Formulationmentioning

confidence: 99%

“…However, their feature measurements are different. SPFW introduces sparse representation into feature selection; [ 28] combines feature selection and adaptive neighbors preservation into a single framework.…”

Section: Basic Idea and Formulationmentioning

confidence: 99%

Sparse preserving feature weights learning

2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Unsupervised feature selection fails to extract more discriminative features which may yield worse performance. Semisupervised feature selection focuses on maximizing data effectiveness by using labeled and unlabeled data together [7]. In this case, the amount of unlabeled data is much larger than that of labeled data.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised feature selection for audio classification based on constraint compensated Laplacian score

Yang¹,

Qu³

et al. 2016

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

Audio classification, classifying audio segments into broad categories such as speech, non-speech, and silence, is an important front-end problem in speech signal processing. Dozens of features have been proposed for audio classification. Unfortunately, these features are not directly complementary and combining them does not improve classification performance. Feature selection provides an effective mechanism for choosing the most relevant and least redundant features for classification. In this paper, we present a semi-supervised feature selection algorithm named Constraint Compensated Laplacian score (CCLS), which takes advantage of the local geometrical structure of unlabeled data as well as constraint information from labeled data. We apply this method to the audio classification task and compare it with other known feature selection methods. Experimental results demonstrate that CCLS gives substantial improvement.

show abstract

“…We emphasize that though we have not treated trained networks directly, our techniques apply to trained networks that are close to GPs. Shown in [9,10,35], randomly initialized infinite width NNs are GPs, and further, the GP property persists under appropriate training [9,28,29,35,36]. Such trained networks at finite width should be effectively described using our techniques, which only requires being close to a GP.…”

Section: Related Workmentioning

confidence: 99%

A study of field theories via neural networks

Maiti

View full text Add to dashboard Cite

show abstract

Locality preserving score for joint feature weights learning

Cited by 10 publications

References 26 publications

Sparse preserving feature weights learning

Sparse preserving feature weights learning

Semi-supervised feature selection for audio classification based on constraint compensated Laplacian score

A study of field theories via neural networks

Contact Info

Product

Resources

About