Novel multi-feature Bag-of-Words descriptor via subspace random projection for efficient human-action recognition

Maqueda, Ana I.; Ruano, Arturo; del-Blanco, Carlos R.; Carballeira, Pablo; Jaureguizar, Fernando; García, Narciso

doi:10.1109/avss.2015.7301736

Cited by 2 publications

(1 citation statement)

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“…Because LDA is adopted, SRP and SSRP may easily overfit the data in the presence of labeling noise and are not applicable for non-linear problems [49]. The BoW model is a simplified representation used in natural language processing and computer vision [16], [50], [51]. The number of words in a BoW model usually reaches tens of millions, which explodes the dimensionality of predictor matrices.…”

Section: A General-purpose Methodsmentioning

confidence: 99%

Comparison among dimensionality reduction techniques based on Random Projection for cancer classification

Xie

Zhang

et al. 2016

Computational Biology and Chemistry

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Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. However, the performance of RP is usually lower. We attempt to improve classification accuracy of RP through combining other reduction dimension methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Feature Selection (FS). We compared classification accuracy and running time of different combination methods on three microarray datasets and a simulation dataset. Experimental results show a remarkable improvement of 14.77% in classification accuracy of FS followed by RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield a more discriminative subspace with an increase of 13.65% on classification accuracy on the same dataset. FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.

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