The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The proximal classifier with consistency (PCC) isan improvement of generalized eigenvalue proximal support vector machine (GEPSVM), ensuring consistency ignored inGEPSVM. However, similar to many other machine learning methods, PCC uses only the global information and the eigenvalue problem need to be solved, which can not classify small sample size (SSS) problem effectively. By exploiting both global...
Comparing and classifying graphs represent two essential steps for network analysis, across different scientific and applicative domains. Here we deal with both operations by introducing the Hamming-Ipsen-Mikhailov (HIM) distance, a novel metric to quantitatively measure the difference between two graphs sharing the same vertices. The new measure combines the local Hamming edit distance and the global...
Non-rigid 3D shape retrieval is an active and important research topic in content based object retrieval. This problem is often cast in terms of the shapes intrinsic geometry due to its invariance to a wide range of non-rigid deformations. In this paper, we devise a novel generative model for shape retrieval based on the spectral representation of the Laplacian of a mesh. Contrary to common use, our...
We propose a series of data analysis methods for both supervised and un-supervised learning techniques. Three objectives of data relationship and characteristics are used to establish a uniform framework of our proposed methods, which are inspired by principal component analysis and linear discriminant analysis. By using the three objectives and some combinations of them, we investigate and illustrate...
In this paper, we provide a new viewpoint of sequential random processes of the kind F(x), where x is a multivariate vector of covariates, in terms of a smoothing operation governed by a covariance function. By exploiting the eigenvalues and eigenvectors of the covariance function, we represent the smooth function in terms of an orthogonal series over basis functions where the basis function weights...
We consider the problem of the assignment of nodes into communities from a set of hyperedges, where every hyperedge is a noisy observation of the community assignment of the adjacent nodes. We focus in particular on the sparse regime where the number of edges is of the same order as the number of vertices. We propose a spectral method based on a generalization of the non-backtracking Hashimoto matrix...
The parametric array loudspeaker (PAL) is well known for its ability to radiate a narrow sound beam from a relatively small ultrasonic emitter. Nonlinear distortions commonly occur in the self-demodulated sound of the PAL. Based on the Volterra filter modeling the self-demodulation process of the PAL, a linearization system can be developed for the PAL. However, the computational complexity of the...
New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a frequency domain related to the structure of the graph. The schemes are designed within the framework of linear shift-invariant graph filters and consider that the seeding signals are injected only at a subset...
OCSVM (one-class support vector machine) is a variant of SVM which only use positive class sample set in training. Since only positive samples can be used in OCSVM, Fully exploiting and using the features of the training samples is of great significance to improve its classification performance. Thus, two aspects of study on kernels have been done in this paper: first, we propose a kernel constructing...
We propose a distributed regression algorithm with the capability of automatically calibrating its parameters during its on-line functioning. The estimation procedure corresponds to a Regularization Network, i.e., the structural form of the estimator is a linear combination of basis functions which coefficients are computed by solving a linear system. The automatic tuning strategy instead constructs...
The principal component analysis (PCA) is a well-known technique to detect, isolate and estimate faults affecting a system. However, PCA identifies only linear structures in a given dataset. In this paper, we propose a new technique to estimate the fault affecting nonlinear systems, within the frame of kernel machines. To this end, the kernel methods are combined to the PCA, the so-called kernel PCA...
Necessary and sufficient conditions are presented for the problem of model matching by non-regular static state feedback to have a solution. These conditions are expressed in terms of polynomial matrix equations and yield a construction of a matching feedback law. The problem is solved in full generality and the cases of special interest are subsequently discussed.
This paper proposes a new control synthesis approach for the stabilization of boundary-controlled parabolic partial differential equations (PDEs). In the proposed approach, the optimal boundary control is expressed in integral state feedback form with quadratic kernel function, where the quadratic's coefficients are decision variables to be optimized. We introduce a system cost functional to penalize...
Dynamic texture (DT) is a simple yet powerful paradigm to model videos with repetitive spatiotemporal behavior. In this paper we propose a novel nonlinear approach for modeling complex DTs based on Koopman operator theoretic method. Koopman operator is linear but infinite dimensional operator, and captures full nonlinear behavior. We exploit this aspect to construct a linear stochastic system in Koopman...
We aim to understand and characterize embeddings of datasets with small anomalous clusters using the Laplacian Eigenmaps algorithm. To do this, we characterize the order in which eigenvectors of a disjoint graph Laplacian emerge and the support of those eigenvectors. We then extend this characterization to weakly connected graphs with clusters of differing sizes, utilizing the theory of invariant...
We refer to eigenfunctions of the kernel corresponding to truncation in a time interval followed by truncation in a frequency band as bandpass prelates (BPPs). We prove frame bounds for certain families of shifts of bandpass prolates, and we numerically construct dual frames for finite dimensional analogues. In the continuous case, the corresponding families produce wavelet frames for the space of...
This paper describes Principal Component Analysis (PCA) used for pre-processing data before training artificial neural networks. Interpretation of the pre-processed data is attempted for time-series data and it is argued that the principal components extracted by linear PCA have an interpretation in the frequency domain. Results are cited showing that a frequency domain interpretation of the eigenvalues...
We examine the promising approach of using Time-Frequency Analysis based on Cohen's Class to characterize the unstable operation (stall) of an axial compressor. Stall precursors are time-localized transients which indicate a coming stall inception. The analysis uses the acoustic vibration signal to reveal the non-stationary behavior when approaching the stall region. The results in this paper show...
In this work, we design complete orthonormal basis functions, which are referred to as optimal basis functions, that span the vector sum of subspaces formed by band-limited spatially concentrated and space-limited spectrally concentrated functions. The optimal basis are shown to be a linear combination of band-limited functions with maximized energy concentration in some spatial region of interest...
We study the problem of learning constitutive features for the effective representation of graph signals, which can be considered as observations collected on different graph topologies. We propose to learn graph atoms and build graph dictionaries that provide sparse representations for classes of signals, which share common spectral characteristics but reside on the vertices of different graphs....
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.