6 accepted papers
Most previous handcrafted keypoint methods focus on designing specific structural patterns using human-defined knowledge. These methods, however, ignore the fact that whether they have enough flexibility to harvest diverse local structures. Recently, the semi-handcrafted approaches based on sparse c…
In this paper, we study the property of frequency shift in two-dimensional Fractional Fourier Transform (2D-FRFT) domain. Based on the mathematical verification and computer simulations, it is demonstrated that the magnitude of reconstruction from phase information satisfies frequency shift-invarian…
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart-be…
This paper proposes a novel Discriminative Feature Selection Guided Deep Canonical Correlation Analysis (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CCA) for multiview learning. The proposed (D <sup xmlns:mml="http://www.w3.org/1998/Math/M…
As an information fusion tool, Kernel Entropy Component Analysis (KECA) is realized by using descriptor of information entropy and optimized by entropy estimation. However, as an unsuper-vised method, it merely puts the information or features from different channels together without considering the…
In this paper, we propose Deep Discriminative Canonical Correlation Analysis (DDCCA), a method to learn the nonlinear transformation of two data sets such that the within-class correlation is maximized and the inter-class correlation is minimized. Parameters of the two deep transformations are joint…