Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning. They can deal with non-hyperspherical robust to outliers. However, the runtime of density-based is heavily dominated by neighborhood finding density estimation which time-consuming. Meanwhile, traditional acceleration methods using indexing techniques such as KD-tree may not...

A new class of large-sample covariance and spectral density matrix estimators is proposed based on the notion of flat-top kernels. The new estimators are shown to be higher-order accurate when higher-order accuracy is possible. A discussion on kernel choice is presented as well as a supporting finite-sample simulation. The problem of spectral estimation under a potential lack of finite fourth m...

The minimum error entropy (MEE) criterion has been receiving increasing attention due to its promising perspectives for applications in signal processing and machine learning. In the context of Bayesian estimation, the MEE criterion is concerned with the estimation of a certain random variable based on another random variable, so that the error’s entropy is minimized. Several theoretical result...

A recent work explicitly models the discontinuous motion estimation problem in the frequency domain where the motion parameters are estimated using a harmonic retrieval approach. The vertical and horizontal components of the motion are independently estimated from the locations of the peaks of respective periodogram analyses and they are paired to obtain the motion vectors using a procedure pro...

Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and will release all software and evaluation...

We devise a new approach for non-parametric adaptive spectral analysis method, which is called the Adaptive Tuning Amplitude and Phase Estimation (ATAPES) method. The main advantage of the ATAPES algorithm is its elimination of biased estimation results in APES method, which is biased peak location and corresponding biased amplitude estimation problem. Therefore, ATAPES method provides more acc...

Local vehicle density state estimation is increasingly becoming an important factor of many Vehicular Ad-hoc Networks (VANETs) applications such as traffic state estimation and protocols such as congestion control. This estimation is used to get an estimated number of the neighbors within the transmission range. The periodic sent messages (beacons) are not sufficient to cover transmission range...