A method based on the Genetic Algorithm (GA) was developed to study the phase behavior of multicomponent and multiphase systems. Upon application of the GA to the thermodynamic models which are commonly used to study the VLE, VLLE and LLE phase equilibria, the physically meaningful values for the Binary Interaction Parameters (BIP) of the models were obtained. Using the method proposed in t...

The problem of face detection remains challenging because faces are non-rigid objects that have a high degree of variability with respect to head rotation, illumination, facial expression, occlusion, and aging. This paper employs a novel technique, known as Locally Linear Embedding (LLE), for solving the face detection problem. The LLE method performs dimensionality reduction on data for learni...

Automatic head frontal-view identification is challenging due to appearance variations caused by pose changes, especially without any training samples. In this paper, we present an unsupervised algorithm for identifying frontal view among multiple facial images under various yaw poses (derived from the same person). Our approach is based on Locally Linear Embedding (LLE), with the assumption th...

Dimensionality reduction is the process by which a set of data points in a higher dimensional space are mapped to a lower dimension while maintaining certain properties of these points relative to each other. One important property is the preservation of the three angles formed by a triangle consisting of three neighboring points in the high dimensional space. If this property is maintained for...

Liquid-liquid extraction-thin layer chromatography (LLE-TLC) has been a common and routine combined method for detection of drugs in biological materials. Solid-phase extraction (SPE) is gradually replacing the traditional LLE method. High performance thin layer chromatography (HPTLC) has several advantages over TLC. The present work studied the higher efficiency of a new SPE-HPTLC method over ...

The locally linear embedding (LLE) algorithm is an unsupervised technique recently proposed for nonlinear dimensionality reduction. In this paper, we describe its supervised variant (SLLE). This is a conceptually new method, where class membership information is used to map overlapping high dimensional data into disjoint clusters in the embedded space. In experiments, we combined it with suppor...

R. M. Shiffrin, R. Ratcliff, K. Murnane, and P. Nobel (1993) claimed that TODAM (a theory of distributed associative memory) is unable simultaneously to predict an absent (or negative) liststrength effect (LSE) and a positive list-length effect (LLE). However, Shiffrin et al. failed to distinguish between situations in which lag (number of items intervening between study and test) is controlled...

The locally linear embedding (LLE) algorithm has recently emerged as a promising technique for nonlinear dimensionality reduction of high-dimensional data. One of its advantages over many similar methods is that only one parameter has to be defined, but no guidance was yet given how to choose it. We propose a hierarchical method for automatic selection of an optimal parameter value. Our approac...

Neighborhood Preserving Embedding (NPE) is a subspace learning algorithm. Since NPE is a linear approximation to Locally Linear Embedding (LLE) algorithm, it has good neighborhood-preserving properties. Although NPE has been applied in many fields, it has limitations to solve recognition task. In this paper, a novel subspace method, named Kernel Fisher Neighborhood Preserving Embedding (KFNPE),...