Laplacian eigenmap algorithm is a typical nonlinear model for dimensionality reduction in classical machine learning. We propose an efficient quantum Laplacian eigenmap algorithm to exponentially speed up the original counterparts. In our work, we demonstrate that the Hermitian chain product proposed in quantum linear discriminant analysis (arXiv:1510.00113,2015) can be applied to implement qua...

in this work, thermal degradation behavior of a fuel-rich energetic mixture containing epoxy binder was studied by thrmogravimetric analysis and differential scanning calorimetry under dynamic nitrogen atmosphere at different heating rates. variation of the thermal degradation activation energy of the mixture was evaluated by differential and integral isoconversional methods via akts software p...

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

This paper studies content-based video retrieval using the combination of audio and visual features. The visual feature is extracted by an adaptive video indexing technique that places a strong emphasis on accurate characterization of spatio-temporal information within video clips. Audio feature is extracted by a statistical time-frequency analysis method that applies Laplacian mixture models t...

for a simple digraph $g$ of order $n$ with vertex set${v_1,v_2,ldots, v_n}$, let $d_i^+$ and $d_i^-$ denote theout-degree and in-degree of a vertex $v_i$ in $g$, respectively. let$d^+(g)=diag(d_1^+,d_2^+,ldots,d_n^+)$ and$d^-(g)=diag(d_1^-,d_2^-,ldots,d_n^-)$. in this paper we introduce$widetilde{sl}(g)=widetilde{d}(g)-s(g)$ to be a new kind of skewlaplacian matrix of $g$, where $widetilde{d}(g...

Medley filters are defined as convex combinations of elementary smoothing filters (averaging, median) with different smoothing bandwidths. It is shown that when adaptive weights of such a mixture are evaluated using the recently proposed Bayesian rules, one obtains a tool which often outperforms the state-of-the-art wavelet-based smoothing algorithms. Additionally, unlike wavelet-based procedur...

A generalization of the commonly used Maximum Likelihood based learning algorithm for the logistic regression model is considered. It is well known that using the Laplace prior (L1 penalty) on model coefficients leads to a variable selection effect, when most of the coefficients vanish. It is argued that variable selection is not always desirable; it is often better to group correlated variable...

Let G^s be a signed graph, where G = (V;E) is the underlying simple graph and s : E(G) to {+, -} is the sign function on E(G). In this paper, we obtain k-th signed spectral moment and k-th signed Laplacian spectral moment of Gs together with coefficients of their signed characteristic polynomial and signed Laplacian characteristic polynomial are calculated.

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appea...