As large-scale multivariate time series data become increasingly common in application domains, such as health care and traffic analysis, researchers are challenged to build efficient tools to analyze it and provide useful insights. Similarity search, as a basic operator for many machine learning and data mining algorithms, has been extensively studied before, leading to several efficient solutions. However, similarity search for multivariate time series data is intrinsically challenging because (1) there is no conclusive agreement on what is a good similarity metric for multivariate time series data and (2) calculating similarity scores between two time series is often computationally expensive. In this paper, we address this problem by applying a generalized hashing framework, namely kernelized locality sensitive hashing, to accelerate time series similarity search with a series of representative similarity metrics. Experiment results on three large-scale clinical data sets demonstrate the effectiveness of the proposed approach.

We address the problem of structure learning of human motion in order to recognize actions from a continuous monocular motion sequence of an arbitrary person from an arbitrary viewpoint. Human motion sequences are represented by multivariate time series in the joint-trajectories space. Under this structured time series framework, we first propose Kernelized Temporal Cut (KTC), an extension of previous works on change-point detection by incorporating Hilbert space embedding of distributions, to handle the nonparametric and high dimensionality issues of human motions. Experimental results demonstrate the effectiveness of our approach, which yields realtime segmentation, and produces high action segmentation accuracy. Second, a spatio-temporal manifold framework is proposed to model the latent structure of time series data. Then an efficient spatio-temporal alignment algorithm Dynamic Manifold Warping (DMW) is proposed for multivariate time series to calculate motion similarity between action sequences (segments). Furthermore, by combining the temporal segmentation algorithm and the alignment algorithm, online human action recognition can be performed by associating a few labeled examples from motion capture data. The results on human motion capture data and 3D depth sensor data demonstrate the effectiveness of the proposed approach in automatically segmenting and recognizing motion sequences, and its ability to handle noisy and partially occluded data, in the transfer learning module.

We address the problem of learning view-invariant 3D models of human motion from motion capture data, in order to recognize human actions from a monocular video sequence with arbitrary viewpoint. We propose a Spatio-Temporal Manifold (STM) model to analyze non-linear multivariate time series with latent spatial structure and apply it to recognize actions in the joint-trajectories space. Based on STM, a novel alignment algorithm Dynamic Manifold Warping (DMW) and a robust motion similarity metric are proposed for human action sequences, both in 2D and 3D. DMW extends previous works on spatio-temporal alignment by incorporating manifold learning. We evaluate and compare the approach to state-of-the-art methods on motion capture data and realistic videos. Experimental results demonstrate the effectiveness of our approach, which yields visually appealing alignment results, produces higher action recognition accuracy, and can recognize actions from arbitrary views with partial occlusion.

OVSF-CDMA systems reallocate user codes when new calls arrive. To reduce code blocking due to new user codes, Generalized Optimal Right First Dynamic Code Assignment (GRFDCA) is brought up. GRFDCA keeps OVSF code trees in optimal right first (RF) states with respect to the number- topology (NT) count. Dynamic model of OVSF-CDMA systems is established. Tests of hypothesis about the Markovity of arrival processes are set up. Based on the Markovity assumption, continuous time Markov model of OVSF-CDMA systems is presented. By introducing absorbing states, phase type (PH) model is applied. Dynamic properties of OVSF-CDMA systems are analyzed based on PH model. Simulations and further discussions based on the dynamic model are presented.

In this paper, we propose Sparse Non-negative Pattern Learning (SNPL) based on self-taught learning framework. In the algorithm, visual patterns are first learned from unlabeled data by non-negative matrix approximation with sparseness constraints, and then features are extracted by the second part of the algorithm, a conjugate family based non-negative sparse feature extraction method. By combining sparse and non-negative constraints of patterns together, SNPL model gives a better representation for images than state-of-art methods. Beyond that, we give an analytical solution for feature extraction although it is approximate, and thereby we extract the features for self-taught learning framework in a faster and more stable way. We apply the new model to various areas, including pattern coding, feature extraction, and recognition. Experimental results show the advantages of SNPL model.

The OVSF-CDMA system must allocate user codes to support a new call, when it supports variable user data rates. Accordingly, the user code would be easily blocked without an efficient code reassignment algorithm. This paper presents a right first dynamic code assignment (RFDCA) algorithm, which aims at always keeping the OVSF codes tree in an optimal right first (RF) state on the basis of the number-topology count (Nt). This algorithm realizes a quick assignment of the OVSF codes to the new callers so that the reassignment of OVSF code could be minimized when new calls arrive, further improving the spectral efficiency of the systems. In this paper, we have analyzed the algorithm by adopting Markov process and have practiced the simulation experiment. The theoretical analysis and the simulation experiment acquire the same results, proving the effectiveness of the algorithm. More importantly, the result of the simulation experiment indicates the number of reassigned codes (NRC) is far fewer than that of other algorithms when new calls arrive

Robustness and discriminability are two key issues in face recognition. In this paper, we propose a new algorithm which extracts micro-structural Gabor feature to achieve good robustness and discriminability simultaneously. We first design a family of directional block partitions to compute the block-level directional projections of the classical Gabor feature. Then we use two statistical kernels, i.e, the mean kernel and the variance kernel, to extract the micro-structural statistics. Analysis of both robustness and discriminability is conducted to show that the new feature is not only more robust to misalignment, but also more discriminative than the classical down-sampling Gabor feature, which is further demonstrated by three groups of experiments on the BANCA dataset.