We have characterized selectivity and sparseness in anterior inferotemporal cortex, using a large data set. Responses were collected from 674 monkey inferotemporal cells, each stimulated by 806 object photographs. This 806 × 674 matrix was examined in two ways: columnwise, looking at responses of a single neuron to all images (single-neuron selectivity), and rowwise, looking at the responses of...

Non-negative matrix factorization (NMF) is a fundamental theory that has received much attention and widely used in image engineering, pattern recognition other fields. However, the classical NMF limitations such as only focusing on local information, sensitivity to noise small sample size (SSS) problems. Therefore, how develop improve performance robustness of algorithm worthy challenge. Based...

An avoidance problem of configurations in 4-cycle systems is investigated by generalizing the notion of sparseness, which is originally from Erdős’ r-sparse conjecture on Steiner triple systems. A 4-cycle system of order v, 4CS(v), is said to be r-sparse if for every integer j satisfying 2 ≤ j ≤ r it contains no configurations consisting of j 4-cycles whose union contains precisely j + 3 vertic...

In this paper we introduce a new sparseness inducing prior which does not involve any (hyper)parameters that need to be adjusted or estimated. Although other applications are possible, we focus here on supervised learning problems: regression and classification. Experiments with several publicly available benchmark data sets show that the proposed approach yields state-of-the-art performance. I...

In this chapter we examine two different settings in which sparseness can be important in a functional data analysis (FDA). The first setting involves sparseness in the functions. The classical assumption of FDA is that each function has been measured at all time points. However, in practice it is often the case that the functions have only been observed at a relatively small number of points. ...

Large sparse datasets are common in many realworld applications. Linear SVM has been shown to be very efficient for classifying such datasets. However, it is still unknown how data sparseness would affect its convergence behavior. To study this problem in a systematic manner, we propose a novel approach to generate large and sparse data from real-world datasets, using statistical inference and ...

It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can resul...

PURPOSE To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. METHODS Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by...