The 22 nd International Conference on Machine Learning
نویسندگان
چکیده
In this paper temporal local tangent space alignment is proposed to deal with time-dependent data, such as video and motion capture data. It is an extension of local tangent space alignment, for short, LTSA, from spacial to temporal learning. LTSA is a nonlinear dimension reduction method based on Euclidean distance. Temporal LTSA, however, is dependent on the continuity of time of input data. Another algorithmic improvement is made upon LTSA for mapping new data between the lowand high-dimensional spaces, which makes LTSA suitable in a changing, dynamic environment. When temporal LTSA is applied to time-dependent data, motion of objects underlying in such data can be carefully analyzed in a low-dimensional space. Motion decomposition and synthesis can be further made for real applications.
منابع مشابه
The 22 nd International Conference on Machine Learning
Support Vector Machines (SVMs) have been one of the major breakthroughs in machine learning, both in terms of their practical success as well as their learning-theoretic properties. This talk presents a generic extension of SVM classification to the case of structured classification, i.e. the task of predicting output variables with some meaningful internal structure. As we will show, this appr...
متن کاملAutomatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique
The quality of the road pavement has always been one of the major concerns for governments around the world. Cracks in the asphalt are one of the most common road tensions that generally threaten the safety of roads and highways. In recent years, automated inspection methods such as image and video processing have been considered due to the high cost and error of manual metho...
متن کاملThe Ninth International Conference on Machine Learning
■ The Ninth International Conference on Machine Learning was held in Aberdeen, Scotland, from 1–3 July 1992, with 198 participants in attendance. The conference covered a broad range of topics drawn from the general area of machine learning, including concept-learning algorithms, clustering, speedup learning, formal analysis of learning systems, neural networks, genetic algorithms, and applicat...
متن کامل