Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces.
Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.
Continue Reading
Industrial manufacturing processes today show an exponential complexity growth rate and the human operator are flooded by an overwhelming complexity and information. In addition, the higher and higher production rates…
In recent years, high-performance mobile devices such as smart phones and tablet devices spread rapidly. They have attracted attention as a new platform for parallel and distributed applications. Based on…
This paper presents a convex geometry (CG)-based method for blind separation of nonnegative sources. First, the unaccessible source matrix is normalized to be column-sum-to-one by mapping the available observation matrix.…
We propose a new type of add-drop Micro ring resonator that is made up of gain and loss materials. In all-dielectric linear systems, light transmission is reciprocal: A symmetric transmission…
We investigate the tracking of 2-D human poses in a video stream to determine the spatial configuration of body parts in each frame, but this is not a trivial task…
An emulation method using Fourier transform and Fresnel theory is utilized to characterize beam propagation through free-space turbulence link. A spatial light modulator is utilized for the generation of phase…
Energy efficiency has recently become a major issue in large data centers due to financial and environmental concerns. This paper proposes an integrated energy-aware resource provisioning framework for cloud data…
The second order transverse autocorrelation technique, using a nonlinear crystal with a random-size and distribution of antiparallel nonlinear domains, has been recently proved to be an effective method for ultrashort…
The increasing volumes of relational data let us find an alternative to cope with them. The Hadoop framework - which is an open source project based on the MapReduce paradigm…