Rank-Sparsity Incoherence for Matrix Decomposition

Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Such a problem arises in a number of applications in model and system identification, and is NP-hard in general. In this paper we consider a convex optimization formulation to splitting the specified matrix into its components, by minimizing a linear combination of the $\ell_1$ norm and the nuclear norm of the components. We develop a notion of \emph{rank-sparsity incoherence}, expressed as an uncertainty principle between the sparsity pattern of a matrix and its row and column spaces, and use it to characterize both fundamental identifiability as well as (deterministic) sufficient conditions for exact recovery. Our analysis is geometric in nature, with the tangent spaces to the algebraic varieties of sparse and low-rank matrices playing a prominent role. When the sparse and low-rank matrices are drawn from certain natural random ensembles, we show that the sufficient conditions for exact recovery are satisfied with high probability. We conclude with simulation results on synthetic matrix decomposition problems

Improving Ieee 802.11 Wlan Handoff Latency by Access Point-Based Modification

IEEE 802.11 WLAN provides multimedia services like live telecast, video streaming, video conferencing, Voice over IP (VoIP) to its users. For deployment of these fast real time services, it needs stringent Quality of service (QoS) requirement such as delay time less than 150ms for VoIP, and packet loss rate of 1%. The mobility service for users come with cost of handoff process required when mobile stations get connected from 1 Access point (AP) to another for continuous service. In existing 802.11 IEEE handoff procedure, the scanning phase can exceed duration of 200ms and packet loss can exceed 10%. Thus, proposed methodology focuses on achieving reduced overall handoff latency by implementing handoff delay duration less than 150ms which is the need for seamless service in IEEE 802.11 WLAN

Belief Propagation and LP Relaxation for Weighted Matching in General Graphs

Loopy belief propagation has been employed in a wide variety of applications with great empirical success, but it comes with few theoretical guarantees. In this paper, we analyze the performance of the max-product form of belief propagation for the weighted matching problem on general graphs. We show that the performance of max-product is exactly characterized by the natural linear programming (LP) relaxation of the problem. In particular, we first show that if the LP relaxation has no fractional optima then max-product always converges to the correct answer. This establishes the extension of the recent result by Bayati, Shah and Sharma, which considered bipartite graphs, to general graphs. Perhaps more interestingly, we also establish a tight converse, namely that the presence of any fractional LP optimum implies that max-product will fail to yield useful estimates on some of the edges. We extend our results to the weighted b-matching and r -edge-cover problems. We also demonstrate how to simplify the max-product message-update equations for weighted matching, making it easily deployable in distributed settings like wireless or sensor networks.National Science Foundation (U.S.) (Grant CAREER 0954059)National Science Foundation (U.S.) (Grant 0964391

Iris Recognition System for Biometric Recognition

Volume 7 Issue 9 (September 201

Aerosol information content analysis of multi-angle high spectral resolution measurements and its benefit for high accuracy greenhouse gas retrievals

New generations of space-borne spectrometers for the retrieval of atmospheric abundances of greenhouse gases require unprecedented accuracies as atmospheric variability of long-lived gases is very low. These instruments, such as GOSAT and OCO-2, typically use a high spectral resolution oxygen channel (O_2 A-band) in addition to CO_2 and CH_4 channels to discriminate changes in the photon path-length distribution from actual trace gas amount changes. Inaccurate knowledge of the photon path-length distribution, determined by scatterers in the atmosphere, is the prime source of systematic biases in the retrieval. In this paper, we investigate the combined aerosol and greenhouse gas retrieval using multiple satellite viewing angles simultaneously. We find that this method, hitherto only applied in multi-angle imagery such as from POLDER or MISR, greatly enhances the ability to retrieve aerosol properties by 2–3 degrees of freedom. We find that the improved capability to retrieve aerosol parameters significantly reduces interference errors introduced into retrieved CO_2 and CH_4 total column averages. Instead of focussing solely on improvements in spectral and spatial resolution, signal-to-noise ratios or sampling frequency, multiple angles reduce uncertainty in space based greenhouse gas retrievals more effectively and provide a new potential for dedicated aerosols retrievals

Solving Sudoku from an Image using Modular Architecture Approach

Sudoku puzzles can be found in various physical forms in newspapers, magazines and elsewhere. It may often be desirable to convert this puzzle into a digital format for ease of solving. This paper proposes a method for extracting and solving a Sudoku puzzle captured in an image. AI techniques can then be applied to solve the Sudoku puzzle. A modular architecture is created for this purpose. Modules can be replaced as needed, making it easier to improve and maintain an application using the proposed architecture DOI: 10.17762/ijritcc2321-8169.150312

Toward Understanding Privileged Features Distillation in Learning-to-Rank

In learning-to-rank problems, a privileged feature is one that is available during model training, but not available at test time. Such features naturally arise in merchandised recommendation systems; for instance, "user clicked this item" as a feature is predictive of "user purchased this item" in the offline data, but is clearly not available during online serving. Another source of privileged features is those that are too expensive to compute online but feasible to be added offline. Privileged features distillation (PFD) refers to a natural idea: train a "teacher" model using all features (including privileged ones) and then use it to train a "student" model that does not use the privileged features. In this paper, we first study PFD empirically on three public ranking datasets and an industrial-scale ranking problem derived from Amazon's logs. We show that PFD outperforms several baselines (no-distillation, pretraining-finetuning, self-distillation, and generalized distillation) on all these datasets. Next, we analyze why and when PFD performs well via both empirical ablation studies and theoretical analysis for linear models. Both investigations uncover an interesting non-monotone behavior: as the predictive power of a privileged feature increases, the performance of the resulting student model initially increases but then decreases. We show the reason for the later decreasing performance is that a very predictive privileged teacher produces predictions with high variance, which lead to high variance student estimates and inferior testing performance.Comment: Accepted by NeurIPS 202