Greedy bilateral sketch, completion & smoothing

Copyright 2013 by the authors. Recovering a large low-rank matrix from highly corrupted, incomplete or sparse outlier over- whelmed observations is the crux of various in- triguing statistical problems. We explore the power of "greedy bilateral (GreB)" paradigm in reducing both time and sample complexities for solving these problems. GreB models a low- rank variable as a bilateral factorization, and up- dates the left and right factors in a mutually adap- tive and greedy incremental manner. We de- tail how to model and solve low-rank approx- imation, matrix completion and robust PCA in GreB's paradigm. On their MATLAB implemen- tations, approximating a noisy 104 × 104 matrix of rank 500 with SVD accuracy takes 6s; Movie- Lens10M matrix of size 69878 × 10677 can be completed in 10s from 30% of 107 ratings with RMSE 0.86 on the rest 70%; the low-rank back- ground and sparse moving outliers in a 120×160 video of 500 frames are accurately separated in 1s. This brings 30 to 100 times acceleration in solving these popular statistical problems

Compressed learning

University of Technology, Sydney. Faculty of Engineering and Information Technology.There has been an explosion of data derived from the internet and other digital sources. These data are usually multi-dimensional, massive in volume, frequently incomplete, noisy, and complicated in structure. These "big data" bring new challenges to machine learning (ML), which has historically been designed for small volumes of clearly defined and structured data. In this thesis we propose new methods of "compressed learning", which explore the components and procedures in ML methods that are compressible, in order to improve their robustness, scalability, adaptivity, and performance for big data analysis. We will study novel methodologies that compress different components throughout the learning process, propose more interpretable general compressible structures for big data, and develop effective strategies to leverage these compressible structures to produce highly scalable learning algorithms. We present several new insights into popular learning problems in the context of compressed learning. The theoretical analyses are tested on real data in order to demonstrate the efficacy and efficiency of the methodologies in real-world scenarios. In particular, we propose "manifold elastic net (MEN)" and "double shrinking (DS)" as two fast frameworks extracting low-dimensional sparse features for dimension reduction and manifold learning. These methods compress the features on both their dimension and cardinality, and significantly improve their interpretation and performance in clustering and classification tasks. We study how to derive fewer "anchor points" for representing large datasets in their entirety by proposing "divide-and-conquer anchoring", in which the global solution is rapidly found for near-separable non-negative matrix factorization and completion in a distributed manner. This method represents a compression of the big data itself, rather than features, and the extracted anchors define the structure of the data. Two fast low-rank approximation methods, "bilateral random projections (BRP)" of fast computer closed-form and "greedy bilateral sketch (GreBske)", are proposed based on random projection and greedy augmenting update rules. They can be broadly applied to learning procedures that requires updates of a low-rank matrix variable and result in significant acceleration in performance. We study how to compress noisy data for learning by decomposing it into the sum mixture of low-rank part and sparse part. "GO decomposition (GoDec)" and the "greedy bilateral (GreB)" paradigm are proposed as two efficient approaches to this problem based on randomized and greedy strategies, respectively. Modifications of these two schemes result in novel models and extremely fast algorithms for matrix completion that aim to recover a low-rank matrix from a small number of its entries. In addition, we extend the GoDec problem in order to unmix more than two incoherent structures that are more complicated and expressive than low-rank or sparse matrices. The three proposed variants are not only novel and effective algorithms for motion segmentation in computer vision, multi-label learning, and scoring-function learning in recommendation systems, but also reveal new theoretical insights into these problems. Finally, a compressed learning method termed “compressed labelling (CL) on distilled label sets (DL)" is proposed for solving the three core problems in multi-label learning, namely high-dimensional labels, label correlation modeling, and sample imbalance for each label. By compressing the labels and the number of classifiers in multi-label learning, CL can generate an effective and efficient training algorithm from any single-label classifier

Are object detection assessment criteria ready for maritime computer vision?

Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision

Low Complexity Radar Gesture Recognition Using Synthetic Training Data

Developments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase

Clutter suppression in ultrasound: performance evaluation and review of low-rank and sparse matrix decomposition methods

Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound color flow imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue are one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is a significant and irreplaceable step for many applications of ultrasound CFI. Currently, this task is often performed by singular value decomposition (SVD) of the data matrix. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the dataset. A potential solution to these issues is using decomposition into low-rank and sparse matrices (DLSM) framework. SVD is one of the algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and less computing time. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and that the moving blood component is sparse. In this paper, we present a comprehensive review of ultrasound clutter suppression techniques and exploit the feasibility of low-rank and sparse decomposition schemes in ultrasound clutter suppression. We conduct this review study by adapting 106 DLSM algorithms and validating them against simulation, phantom, and in vivo rat datasets. Two conventional quality metrics, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression

Electromagnetic Thermography Nondestructive Evaluation: Physics-based Modeling and Pattern Mining

Electromagnetic mechanism of Joule heating and thermal conduction on conductive material characterization broadens their scope for implementation in real thermography based Nondestructive testing and evaluation (NDT&E) systems by imparting sensitivity, conformability and allowing fast and imaging detection, which is necessary for efficiency. The issue of automatic material evaluation has not been fully addressed by researchers and it marks a crucial first step to analyzing the structural health of the material, which in turn sheds light on understanding the production of the defects mechanisms. In this study, we bridge the gap between the physics world and mathematical modeling world. We generate physics-mathematical modeling and mining route in the spatial-, time-, frequency-, and sparse-pattern domains. This is a significant step towards realizing the deeper insight in electromagnetic thermography (EMT) and automatic defect identification. This renders the EMT a promising candidate for the highly efficient and yet flexible NDT&E