Complex data classification in weighted accelerated failure time model

Fard, Nasser; Sadeghzadeh, Keivan

doi:10.1109/rams.2016.7448035

Cited by 1 publication

(1 citation statement)

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“…X ji consists of features which can be used to identify the C classes. In the LDA calculation, each feature vector is first normalized to the range of [0, 1] using unity-based normalization before analysis (for each feature, the maximum value among all classes is set to 1, and the minimum value is set to 0) (Fard and Sadeghzadeh, 2016 ). Then the optimal projection direction is determined by calculating the eigenvectors of E = S

S b , where S w , S b are within-class scatter matrix and between-class scatter matrix, respectively (Dudoit et al, 2002 ; Kumar and Ravikanth, 2009 ):…”

Section: Methodsmentioning

confidence: 99%

Coding Properties of Mouse Retinal Ganglion Cells with Dual-Peak Patterns with Respect to Stimulus Intervals

Yan

Gong

Zhang

et al. 2016

Front. Comput. Neurosci.

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How visual information is encoded in spikes of retinal ganglion cells (RGCs) is essential in visual neuroscience. In the present study, we investigated the coding properties of mouse RGCs with dual-peak patterns with respect to visual stimulus intervals. We first analyzed the response properties, and observed that the latencies and spike counts of the two response peaks in the dual-peak pattern exhibited systematic changes with the preceding light-OFF interval. We then applied linear discriminant analysis (LDA) to assess the relative contributions of response characteristics of both peaks in information coding regarding the preceding stimulus interval. It was found that for each peak, the discrimination results were far better than chance level based on either latency or spike count, and were further improved by using the combination of the two parameters. Furthermore, the best discrimination results were obtained when latencies and spike counts of both peaks were considered in combination. In addition, the correct rate for stimulation discrimination was higher when RGC population activity was considered as compare to single neuron's activity, and the correct rate was increased with the group size. These results suggest that rate coding, temporal coding, and population coding are all involved in encoding the different stimulus-interval patterns, and the two response peaks in the dual-peak pattern carry complementary information about stimulus interval.

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S b , where S w , S b are within-class scatter matrix and between-class scatter matrix, respectively (Dudoit et al, 2002 ; Kumar and Ravikanth, 2009 ):…”

Section: Methodsmentioning

confidence: 99%