Search results for "Function"
showing 10 items of 14432 documents
Fuzzy temporal random sets with an application to cell biology
2007
Total Internal Reflection Fluorescence Microscopy (TIRFM) greatly facilitates to imaging the first steps of endocytosis, a process whereby cells traffic cargo from the cell surface to endosomes. Using TIRFM, fluorescent-tagged endocytic proteins are observed as overlapped areas forming random clumps of different sizes, shapes and durations. A common procedure to segment these objects consists of thresholding the original gray-level images to produce binary sequences in which a pixel is covered or not by a given fluorescent-tagged protein. This binary logic is not appropriate because it leaves a free tuning parameter to be set by the user which can influence on the conclusions of the statist…
Psychophysical response to electrocutaneous stimulation.
1984
A method is presented to determine a reliable stimulus-sensation relationship particularly suitable for electrocutaneous stimulation. An experimental intensity-discrimination curve was obtained through simple psychophysical comparison tasks, and sensory response was inferred from integration of a JND's density function. The psychophysical response resembles a power law, although departures cannot be described in terms of a unique exponent. An estimate of binary information capacity per electrode is also given as a feature of a stimulation procedure that preserves a low value of the size-intensity product.
Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap
2015
This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…
A principled approach to network-based classification and data representation
2013
Measures of similarity are fundamental in pattern recognition and data mining. Typically the Euclidean metric is used in this context, weighting all variables equally and therefore assuming equal relevance, which is very rare in real applications. In contrast, given an estimate of a conditional density function, the Fisher information calculated in primary data space implicitly measures the relevance of variables in a principled way by reference to auxiliary data such as class labels. This paper proposes a framework that uses a distance metric based on Fisher information to construct similarity networks that achieve a more informative and principled representation of data. The framework ena…
Spectral clustering with the probabilistic cluster kernel
2015
Abstract This letter introduces a probabilistic cluster kernel for data clustering. The proposed kernel is computed with the composition of dot products between the posterior probabilities obtained via GMM clustering. The kernel is directly learned from the data, is parameter-free, and captures the data manifold structure at different scales. The projections in the kernel space induced by this kernel are useful for general feature extraction purposes and are here exploited in spectral clustering with the canonical k-means. The kernel structure, informative content and optimality are studied. Analysis and performance are illustrated in several real datasets.
Fuzzy sigmoid kernel for support vector classifiers
2004
This Letter proposes the use of the fuzzy sigmoid function presented in (IEEE Trans. Neural Networks 14(6) (2003) 1576) as non-positive semi-definite kernel in the support vector machines framework. The fuzzy sigmoid kernel allows lower computational cost, and higher rate of positive eigenvalues of the kernel matrix, which alleviates current limitations of the sigmoid kernel.
Multi-Authored Manuscripts and Speedup in Academic Publishing
2014
It is unfair to count a n-authored paper as one paper for each coauthor, i.e., as n papers: this is “feeding the multitude”. Sharing the credit among coauthors by percentages or by simply dividing by n is fairer but somewhat harsh. So, we propose to take into account the productivity gains of parallelization by introducing a team bonus function that multiplies the allocation thereby increasing the credit allocated to each coauthor.The degree of parallelization cannot be determined exogenously discipline by discipline. So, one may propose that each team of coauthors indicates how the labor was organized to produce the paper. Unfortunately, the coauthors may systematically bias their answers …
Capacity studies of spatially correlated MIMO Rice channels
2010
In this paper, we have studied the statistical properties of the capacity of spatially correlated multiple-input multiple-output (MIMO) Rice channels. We have derived an exact closed-form expression for the probability density function (PDF) and an exact expression for the cumulative distribution function (CDF) of the channel capacity for single-input multiple-output (SIMO) and multiple-input single-output (MISO) systems. Furthermore, an accurate closed-form expression has been derived for the level-crossing rate (LCR) and an accurate expression has been obtained for the average duration of fades (ADF) of the SIMO and MISO channel capacities. For the MIMO case, we have investigated the PDF,…
Feature extraction from remote sensing data using Kernel Orthonormalized PLS
2007
This paper presents the study of a sparse kernel-based method for non-linear feature extraction in the context of remote sensing classification and regression problems. The so-called kernel orthonormalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse (reduced) approximation for large scale data sets, which ultimately leads to rKOPLS. The method demonstrates good capabilities in terms of expressive power of the extracted features and scalability.
Multi-functional Protein Clustering in PPI Networks
2008
Protein-Protein Interaction (PPI) networks contain valuable information for the isolation of groups of proteins that participate in the same biological function. Many proteins play different roles in the cell by taking part in several processes, but isolating the different processes in which a protein is involved is often a difficult task. In this paper we present a method based on a greedy local search technique to detect functional modules in PPI graphs. The approach is conceived as a generalization of the algorithm PINCoC to generate overlapping clusters of the interaction graph in input. Due to this peculiarity, multi-facets proteins are allowed to belong to different groups correspondi…