Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

Sparse representation-based classification (SRC), proposed by Wright et al., seeks the sparsest decomposition of a test sample over the dictionary of training samples, with classification to the most-contributing class. Because it assumes test samples can be written as linear combinations of their same-class training samples, the success of SRC depends on the size and representativeness of the training set. Our proposed classification algorithm enlarges the training set by using local principal component analysis to approximate the basis vectors of the tangent hyperplane of the class manifold at each training sample. The dictionary in SRC is replaced by a local dictionary that adapts to the test sample and includes training samples and their corresponding tangent basis vectors. We use a synthetic data set and three face databases to demonstrate that this method can achieve higher classification accuracy than SRC in cases of sparse sampling, nonlinear class manifolds, and stringent dimension reduction.Comment: Published in "Computational Intelligence for Pattern Recognition," editors Shyi-Ming Chen and Witold Pedrycz. The original publication is available at http://www.springerlink.co

Formation of cristae and crista junctions in mitochondria depends on antagonism between Fcj1 and Su e/g

Crista junctions (CJs) are important for mitochondrial organization and function, but the molecular basis of their formation and architecture is obscure. We have identified and characterized a mitochondrial membrane protein in yeast, Fcj1 (formation of CJ protein 1), which is specifically enriched in CJs. Cells lacking Fcj1 lack CJs, exhibit concentric stacks of inner membrane in the mitochondrial matrix, and show increased levels of F1FO–ATP synthase (F1FO) supercomplexes. Overexpression of Fcj1 leads to increased CJ formation, branching of cristae, enlargement of CJ diameter, and reduced levels of F1FO supercomplexes. Impairment of F1FO oligomer formation by deletion of its subunits e/g (Su e/g) causes CJ diameter enlargement and reduction of cristae tip numbers and promotes cristae branching. Fcj1 and Su e/g genetically interact. We propose a model in which the antagonism between Fcj1 and Su e/g locally modulates the F1FO oligomeric state, thereby controlling membrane curvature of cristae to generate CJs and cristae tips

Nearest-neighbor based methods for nonlinear time-series analysis

Das Thema dieser Arbeit ist die Anwendung der Nächste-Nachbar-Suche in Verfahren der nichtlinearen Zeitreihenanalyse. Ein Haupteinsatzgebiet der Nächste-Nachbar-Suche in der nichtlinearen Zeitreihenanalyse ist die Modellierung und Vorhersage nichtlinearer dynamischer Systeme. Dazu werden meist skalare Zeitreihen dieser Systeme durch die Technik der Zeitverzögerungsrekonstruktion in einen mehrdimensionalen Zustandsraum eingebettet. Diese Verfahren werden in der Arbeit zusammen mit einer Methode zur Validierung der so gewonnenen Modelle vorgestellt. Als ein zentrales Ergebnis dieser Arbeit wird dann ein effizienter Algorithmus zur Nächsten-Nachbar-Suche, der sogenannte ATRIA (Advanced Triangle Inequality Algorithm), vorgestellt. Dieser zeichnet sich sowohl durch flexible Wahl der zur Distanzberechnung verwendeten Metrik als auch durch die Möglichkeit aus, die Laufzeit des Algorithmus durch eine abgeschwächte Variante der Suche, bei der sogenannte approximative Nächste-Nachbarn bestimmt werden, weiter zu verringern. Weiter werden verschiedene Methoden der Bestimmung des Spektrums der fraktalen Dimensionen eines dynamischen Systems vorgestellt und verglichen. In diesem Zusammenhang werden numerische Untersuchungen zur Ermittlung des Zusammenhangs zwischen Laufzeit des ATRIA und der fraktalen Dimension des Datensatzes, in dem die Nachbarsuche stattfindet, präsentiert. Danach wird ein Verfahren zur lokalen Modellierung raum-zeitlicher dynamischer Systeme vorgestellt. Dieses Verfahren, bei dem Nächste-Nachbarn in einem vergleichsweise hochdimensionalen Raum bestimmt werden müssen, profitiert deutlich von der Verwendung eines Algorithmus, dessen Laufzeit wie die des vorgestellten ATRIA nur unkritisch von der formalen Dimension des Datensatzes abhäng

Pattern Recognition Using Finite-Iteration Cellular Systems

Cellular Systems are defined by cells that have an internal state and local interactions between cells that govern the dynamics of the system. We propose to use a special kind of Cellular Neural Networks (CNNs) which operates in finite iteration discrete-time mode and mimics the processing of visual perception in biological systems for digit recognition. We propose also a solution to another type of pattern recognition problem using a non-standard cellular neural networks called Molecular Graph Networks (MGNs) which offer direct mapping from compound to property of interest such as Physico-Chemical, Toxicity, logP, Inhibitory Activity MGNs translate molecular topology to network topology. We show how to design/train by backpropagation CNNs and MGNs in their discrete-time and finite-iteration versions to perform classification on real-world data sets

Building Ensembles with Heterogeneous Models

In the context of ensemble learning for regression problems, we study the e#ect of building ensembles from di#erent model classes

CNN in drug design : recent developments

We describe a method for construction of specific types of Neural Networks composed of structures directly linked to the structure of the molecule under consideration. Each molecule can be represented by a unique neural connectivity problem (graph) which can be programmed onto a Cellular Neural Network. The idea was to translate chemical structures like small organic molecules or peptides into a self learning environment which is CNN based. In the case of small molecules, each cell of the CNN stands for one atom of the molecule under consideration. But in contrast to the standard CNN architecture where each cell is connected to the neighboring cells, only those cells of the feature net are connected for which there also exists a chemical bond in the molecule under consideration. This implies that the feature net topology varies from molecule to molecule. In the case of peptides, the amino acids that form the building blocks of the peptide are reflected by the CNN cells wherein the amino acid sequence defines the network topology. Unlike the standard CNN used for image processing, there are no input values like the input image that are fed into the feature net. Instead, all information about the input molecule is supplied to the feature net by means of the topology. The output of several feature nets is fed into a supervisor neural network which computes the final output value. The combination of several feature nets and a supervisor networks constitutes the Molecular Graph Network (MGN). The designed networks are used for selection of molecules representing wanted properties such as activity against specific diseases, interactions with other compounds, toxicity etc. and possibly being candidates to be tested further as new drugs