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The phytohormone auxin acts as a prominent signal, providing, by its local accumulation or depletion in selected cells, a spatial and temporal reference for changes in the developmental program. The distribution of auxin depends on both auxin metabolism (biosynthesis, conjugation and degradation) and cellular auxin transport. We identified in silico a novel(More)
For cancer classification problems based on gene expression, the data usually has only a few dozen sizes but has thousands to tens of thousands of genes which could contain a large number of irrelevant genes. A robust feature selection algorithm is required to remove irrelevant genes and choose the informative ones. Support vector data description (SVDD)(More)
Deep networks are well known for their powerful function approximations. To train a deep network efficiently, greedy layer-wise pre-training and fine tuning are required. Typically, pre-training, aiming to initialize a deep network, is implemented via unsupervised feature learning, with multiple feature representations generated. However, in general only(More)
The typical classification rule for kernel sparse representation-based classifier (KSRC) is the reconstruction error minimization rule. Its computational complexity mainly depends on both the dimensionality of a subspace and the number of training samples. This paper presents an alternative classification rule, called reconstruction coefficient energy(More)
The clustering assumption is to maximize the within-cluster similarity and simultaneously to minimize the between-cluster similarity for a given unlabeled dataset. This paper deals with a new spectral clustering algorithm based on a similarity and dissimilarity criterion by incorporating a dissimilarity criterion into the normalized cut criterion. The(More)