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In recent years, nonnegative matrix factorization (NMF) attracts much attention in machine learning and signal processing fields due to its interpretability of data in a low dimensional subspace. For clustering problems, symmetric nonnegative matrix factorization (SNMF) as an extension of NMF factorizes the similarity matrix of data points directly and outperforms NMF when dealing with nonlinear data structure. However, the clustering results of SNMF is very sensitive to noisy data.

This paper presents a novel method to track the hierarchical structure of Web video groups on the basis of salient keyword matching including semantic broadness estimation. To the best of our knowledge, this paper is the first work to perform extraction and tracking of the hierarchical structure simultaneously. Specifically, the proposed method first extracts the hierarchical structure of Web video groups and salient keywords of them on the basis of an improved scheme of our previously reported method.

This paper provides a theoretical analysis on the classification accuracy of LDA-Bayesian based method with respect

Compressed sensing recovery techniques allow for reconstruction of an unknown sparse vector from an underdetermined system of linear equations. Recently, a lot of attention was drawn to the problem of recovering the sparse vector from quantized CS measurements. Especially interesting is the case, when extreme quantization is enforced that captures only the sign of the measurements. The problem becomes even more difficult if the measurements are corrupted by noise. In this paper we consider \ac{AWGN}.

In this work, we are interested in assessing the optimality of the human auditory system, when the input stimuli is natural speech that is affected by additive noise. In order to do this, we consider the DANTALE II listening test paradigm of Wagener et al., which has been used to evaluate the intelligibility of noisy speech by exposing human listeners to a selection of constructed noisy sentences. Inspired by this test, we propose a simple model for the communication and classification of noisy speech that takes place in the test.

In this paper we present experimental results for the development
of a gesture recognition system using a 77 GHz FMCW
radar system. We measure the micro-Doppler signature of a
gesturing hand to construct an energy distribution in velocity
space over time. A gesturing hand is fundamentally a dynamical
system with unobservable “state” (i.e. the name of the gesture)
which determines the sequence of associated observable
velocity-energy distributions, so a Hidden Markov Model is
used to for gesture recognition, a more tailored approach than