We want to use a variety of sparseness measured applied to ‘the minimal `1 norm representation’ of a music signal in an over-complete dictionary as features for automatic classification of music. Unfortunately, the process of computing the optimal `1 norm representation is rather slow, and we therefore investigate the use of matching pursuit, alternating projection, and Moore-Penrose inverse fo...

Blind separation of linearly mixed white Gaussian sources is impossible, due to rotational symmetry. For this reason, all blind separation algorithms are based on some assumption concerning the fashion in which the situation departs from that insoluble case. Here we discuss the assumption of sparseness and try to put various algorithms that make the sparseness assumption in a common framework. ...

There has been significant recent interest in dictionary learning and sparse coding, with applications in denoising, interpolation, feature extraction, and classification [1]–[3]. Increasingly it has been recognized that these models may be improved by imposing additional prior information, beyond sparseness. For example, a locality constraint has been used successfully in the context of featur...

Bilexical information is known to be helpful in parse disambiguation, but the benefit is limited because of lexical sparseness. An approach using word classes can reduce sparseness and potentially leads to more accurate parsing. Firstly, we describe a method identifying the dependency types of the Alpino parser for Dutch to which we would like to apply generalization. These are the types which ...

33 We have characterized selectivity and sparseness in anterior inferotemporal 34 cortex using a large data set. Responses were collected from 674 monkey 35 inferotemporal cells, each stimulated by 806 object photographs. This 806×674 36 matrix was examined in two ways: column-wise, looking at responses of a 37 single neuron to all images (single-neuron selectivity), and row-wise, looking 38 at...

In this thesis several expressive visualization techniques for volumetric data are presented. The key idea is to classify the underlying data according to its prominence on the resulting visualization by importance value. The importance property drives the visualization pipeline to emphasize the most prominent features and to suppress the less relevant ones. The suppression can be realized glob...

Although popular accounts suggest that neurons along the ventral visual processing stream become increasingly selective for particular objects, this appears at odds with the fact that inferior temporal cortical (IT) neurons are broadly tuned. To explore this apparent contradiction, we compared processing in two ventral stream stages (visual cortical areas V4 and IT) in the rhesus macaque monkey...

Sparseness is a useful regularizer for learning in a wide range of applications, in particular in neural networks. This paper proposes a model targeted at classification tasks, where sparse activity and sparse connectivity are used to enhance classification capabilities. The tool for achieving this is a sparseness-enforcing projection operator which finds the closest vector with a pre-defined s...