Noisy Side Information Research Articles

Noisy One-Bit Compressed Sensing With Side-Information

We consider the problem of sparse signal reconstruction from noisy one-bit compressed measurements when the receiver has access to side-information (SI). We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip error after quantization. A generalized approximate message passing-based method for signal reconstruction from noisy one-bit compressed measurements is proposed which is then extended for the case where the receiver has access to a signal that aids signal reconstruction, i.e., side-information. Two different scenarios of SI are considered-a) SI consisting of support information only, and b) SI consisting of support and amplitude information. SI is either a noisy version of the signal or a noisy estimate of the support of the signal. We develop reconstruction algorithms from one-bit measurements using noisy SI available at the receiver. Laplacian distribution and Bernoulli distribution are used to model the two types of noises which when applied to the signal and the support, yield the SI for the above two cases, respectively. The Expectation-Maximization algorithm is used to estimate the noise parameter using noisy one-bit compressed measurements and the SI. We show that one-bit compressed measurement-based signal reconstruction is quite sensitive to noise, and the reconstruction performance can be significantly improved by exploiting available side-information at the receiver.

Tensor completion with noisy side information

We develop a new model for tensor completion which incorporates noisy side information available on the rows and columns of a 3-dimensional tensor. This method learns a low rank representation of the data along with regression coefficients for the observed noisy features. Given this model, we propose an efficient alternating minimization algorithm to find high-quality solutions that scales to large data sets. Through extensive computational experiments, we demonstrate that this method leads to significant gains in out-of-sample accuracy filling in missing values in both simulated and real-world data. We consider the problem of imputing drug response in three large-scale anti-cancer drug screening data sets: the Genomics of Drug Sensitivity in Cancer (GDSC), the Cancer Cell Line Encyclopedia (CCLE), and the Genentech Cell Line Screening Initiative (GCSI). On imputation tasks with 20% to 80% missing data, we show that the proposed method TensorGenomic matches or outperforms state-of-the-art methods including the original tensor model and a multilevel mixed effects model. With 80% missing data, TensorGenomic improves the R^2 from 0.404 to 0.552 in the GDSC data set, 0.407 to 0.524 in the CCLE data set, and 0.331 to 0.453 in the GCSI data set compared to the tensor model which does not take into account genomic side information.

Partial label learning with noisy side information

Partial label learning with noisy side information

Partial multi-label learning with noisy side information

Partial multi-label learning (PML) aims to learn from the training data where each training example is annotated with a candidate label set, among which only a subset is relevant. Despite the success of existing PML approaches, a major drawback of them lies in lacking of robustness to noisy side information. To tackle this problem, we introduce a novel partial multi-label learning with noisy side information approach, which simultaneously removes noisy outliers from the training instances and trains robust partial multi-label classifier for unlabeled instances prediction. Specifically, we first represent the observed sample set as a feature matrix and then decompose it into an ideal feature matrix and an outlier feature matrix by using the low-rank and sparse decomposition scheme, where the former is constrained to be low rank by considering that the noise-free feature information always lies in a low-dimensional subspace and the latter is assumed to be sparse by considering that the outliers are usually sparse among the observed feature matrix. Secondly, we refine an ideal label confidence matrix from the observed label matrix and use the graph Laplacian regularization to constrain the confidence matrix to keep the intrinsic structure among feature vectors. Thirdly, we constrain the feature mapping matrix to be low rank by utilizing the label correlations. Finally, we obtain both the ideal features and ground-truth labels via minimizing the loss function, where the augmented Lagrange multiplier algorithm and quadratic programming are incorporated to solve the optimization problem. Extensive experiments conducted on ten different data sets demonstrate the effectiveness of our proposed method.

Optimization For Energy-Efficient OFDM Amplify and Forward Non-Concurrent Two-Way Relaying

We consider an orthogonal frequency division multiplexing based non-concurrent two-way relaying (ncTWR), where the base station (BS) serves a transmit user (TU) and a receive user (RU), whose receive signal is interfered by the TU transmit signal. The RU uses noisy side-information gathered by overhearing the TU transmit signal to neutralize the interference. We optimize the global energy efficiency (GEE) metric, which is a fractional function of the network sum-rate and its total energy consumption, by using a quadratic transformation-based algorithm. We numerically demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.

Linear Codes for Broadcasting With Noisy Side Information

We consider network coding for a noiseless broadcast channel, where each receiver demands a subset of the messages available at the transmitter and is equipped with noisy side information in the form of an erroneous version of the message symbols it demands. We view the message symbols as elements from a finite field and assume that the number of symbol errors in the noisy side information is upper bounded by a known constant. This communication problem, which we refer to as broadcasting with noisy side information (BNSI) , has applications in the re-transmission phase of downlink networks. We derive a necessary and sufficient condition for a linear coding scheme to satisfy the demands of all the receivers in a given BNSI network, and show that syndrome decoding can be used at the receivers to decode the demanded messages from the received codeword and the available noisy side information. We represent BNSI problems as bipartite graphs, and using this representation, classify the family of problems, where linear coding provides bandwidth savings compared to uncoded transmission. We provide a simple algorithm to determine if a given BNSI network belongs to this family of problems, i.e., to identify if linear coding provides an advantage over uncoded transmission for the given BNSI problem. We provide lower bounds and upper bounds on the optimal codelength and constructions of linear coding schemes based on linear error correcting codes. For any given BNSI problem, we construct an equivalent index coding problem. A linear code is a valid scheme for a BNSI problem if and only if it is valid for the constructed scalar linear index coding problem.

SDDRS: Stacked Discriminative Denoising Auto-Encoder based Recommender System

Recommender systems are widely used in our life for automatically recommending items relevant to our preference. Collaborative Filtering (CF) is one of the most successful methods in recommendation field. Matrix Factorization (MF) based recommender system is designed according to the basic strategy of the CF algorithm, which is widely adopted recently. However, the rating matrix utilized by these models is usually sparse, so it is of vital significance to integrate the side information to provide relatively effective knowledge for modeling the user or item features. The key problem is to extract effective features from the noisy side information. However, the side information contains a lot of noise except rating knowledge, which makes it a challenging issue for extracting effective features. In this paper, we propose Stacked Discriminative Denoising Auto-Encoder based Recommender System (SDDRS) by integrating deep learning model with MF based recommender system to effectively incorporate side information with rating information. Extensive top-N recommendation experiments conducted on three real-world datasets empirically demonstrate that SDDRS outperforms several state-of-the-art methods.

Joint source and channel coding with systematic polar codes for wireless sensor communication in next generation networks

Polar codes have strongly entered into action within the standardization of the next generation 5G mobile communication systems, which are expected to be an enabling technology for the Internet of Things where networks with a large number of sensors have to handle massive connectivity demands. This paper proposes and investigates the use of systematic polar codes for joint source-channel coding of correlated sources in wireless sensor networks, thus allowing the compression of the volume of data to be transmitted over the network on one hand, and on the other hand, the protection of this data from channel impairments. Results show that systematic polar codes can achieve a distributed compression with rates close to theoretical limits, with better error rates obtained for larger blocks, and a better robustness against transmission errors obtained with stronger compression and shorter block lengths. Furthermore, while the system is able to overcome the effect of noise on parity information with adequate power management, noisy side information significantly degrades system performance with remarkable gaps towards the case of distributed compression with an ideal transmission channel.

Partition Level Constrained Clustering.

Constrained clustering uses pre-given knowledge to improve the clustering performance. Here we use a new constraint called partition level side information and propose the Partition Level Constrained Clustering (PLCC) framework, where only a small proportion of the data is given labels to guide the procedure of clustering. Our goal is to find a partition which captures the intrinsic structure from the data itself, and also agrees with the partition level side information. Then we derive the algorithm of partition level side information based on K-means and give its corresponding solution. Further, we extend it to handle multiple side information and design the algorithm of partition level side information for spectral clustering. Extensive experiments demonstrate the effectiveness and efficiency of our method compared to pairwise constrained clustering and ensemble clustering methods, even in the inconsistent cluster number setting, which verifies the superiority of partition level side information to pairwise constraints. Besides, our method has high robustness to noisy side information, and we also validate the performance of our method with multiple side information. Finally, the image cosegmentation application based on saliency-guided side information demonstrates the effectiveness of PLCC as a flexible framework in different domains, even with the unsupervised side information.

Data clustering using side information dependent Chinese restaurant processes

Side information, or auxiliary information associated with documents or image content, provides hints for clustering. We propose a new model, side information dependent Chinese restaurant process, which exploits side information in a Bayesian nonparametric model to improve data clustering. We introduce side information into the framework of distance dependent Chinese restaurant process using a robust decay function to handle noisy side information. The threshold parameter of the decay function is updated automatically in the Gibbs sampling process. A fast inference algorithm is proposed. We evaluate our approach on four datasets: Cora, 20 Newsgroups, NUS-WIDE and one medical dataset. Types of side information explored in this paper include citations, authors, tags, keywords and auxiliary clinical information. The comparison with the state-of-the-art approaches based on standard performance measures (NMI, F1) clearly shows the superiority of our approach.

Source Coding Problems With Conditionally Less Noisy Side Information

A computable expression for Heegard and Berger's rate-distortion function has eluded information theory for nearly three decades. Heegard and Berger's single-letter achievability bound is well known to be optimal for physically degraded side information; however, it is not known whether the bound is optimal for arbitrarily correlated side information (general discrete memoryless sources). In this paper, we consider a new setup where the side information at one receiver is conditionally less noisy than that at the other. The new setup includes degraded side information as a special case, and it is motivated by the literature on degraded and less noisy broadcast channels. Our key contribution is a converse proving the optimality of Heegard and Berger's achievability bound in a new setting, where the side information is conditionally less noisy and one distortion function is deterministic. The less noisy setup is also generalized to two different successive-refinement problems.

Biometric template protection in multimodal authentication systems based on error correcting codes*

The widespread deployment of biometric systems has raised public concern about security and privacy of personal data. In this paper, we present a novel framework for biometric template security in multimodal biometric authentication systems based on error correcting codes. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder based on distributed source coding principles. It is shown that the proposed method binds the biometric template in a cryptographic key which does not reveal any information about the original biometric data even if it is compromised by an attacker. Furthermore, the advantages of the proposed method in terms of security and impact on matching accuracy are discussed. We assess the performance of the proposed method in the context of HUMABIO, an EU Specific Targeted Research Project, where face and gait biometrics are employed in an unobtrusive application scenario for human authentication. Experimental evaluation on a multimodal biometric database demonstrates the validity of the proposed method.

A Channel Coding Approach for Human Authentication From Gait Sequences

Human authentication using biometric traits has become an increasingly important issue in a large range of applications. In this paper, a novel channel coding approach for biometric authentication based on distributed source coding principles is proposed. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder and error correcting codes are employed for user verification. It is shown that the effective exploitation of the noise channel distribution in the decoding process improves performance. Moreover, the proposed method increases the security of the stored biometric templates. As a case study, the proposed framework is employed for the development of a novel gait recognition system based on the extraction of depth data from human silhouettes and a set of discriminative features. Specifically, gait sequences are represented using the radial and the circular integration transforms and features based on weighted Krawtchouk moments. Analytical models are derived for the effective modeling of the correlation channel statistics based on these features and integrated in the soft decoding process of the channel decoder. The experimental results demonstrate the validity of the proposed method over state-of-the-art techniques for gait recognition.

Hyper-trellis decoding of pixel-domain Wyner-Ziv video coding

In this paper, we present a new decoding algorithm for the Wyner-Ziv (WZ) video coding scheme based on turbo codes. In this scheme, a video frame is encoded using a turbo code, and only a subset of the parity bits are sent to the decoder. At the decoder, the temporal correlation of the video sequence is exploited by using the previous frame as noisy side information (SI) for the current frame. However, there is a mismatch between the SI, which is available as pixel values, and the binary code bits. Previous implementations of the decoder use suboptimal approaches that convert pixel values to soft information for code bits. We present a new decoding algorithm for this application based on decoding on a hyper-trellis, in which multiple states of the original code trellis are combined. We show that this approach significantly improves performance without changing the complexity of the decoder. We also introduce a new technique for the WZ decoder to exploit the spatial correlation within a frame without requiring transform-domain encoding at the encoder, thereby reducing its complexity. Simulation results for fixed-rate transmission show a 9-10-dB improvement in the peak signal-to-noise ratio when compared to a WZ video codec that does bitwise decoding and utilizes only the temporal correlation.

공간적 유사성과 심볼단위 오류정정 채널 코드를 이용한 경량화 비디오 부호화 방법

기존의 부호화 기술은 부호화기의 복잡도가 복호화기에 비해 매우 높은 구조로 이루어져 있다. 하지만 최근에 부호화기 복잡도의 대부분을 차지했던 움직임 예측/보상과정을 없애는 경량화 부호화 구조에 대한 연구가 중요해졌다. Wyner-Ziv 부호화 기술은 이의 대표적인 기술로서 부호화기는 단순히 현재 프레임에 대한 패리티 정보만을 생성하며 프레임 간 유사성을 이용하는 어떠한 처리절차도 행하지 않기 때문에 종래의 기술에 비해 매우 간단한 구조를 갖는다. 하지만 Wyner-Ziv 부호화 구조에서는 잡음이 많은 보조영상을 복호화에 이용 할 경우 채널 코드의 복호화 오류가 발생한다. 이러한 복호화 오류는 특히 영상 간 유사성이 적어 보조정보를 잘 만들 수 없는 경우 더 많이 발생하며 복원된 영상에 마치 Salt & Pepper와 같은 형태의 잡음으로 나타난다. 이러한 잡음은 비록 그 발생빈도가 적더라도 복원된 영상의 주관적인 화질을 상당히 떨어뜨리는 요소로 작용하므로 이전에는 공간적 유사성을 이용하여 이러한 오류를 정정하는 선택적 미디언 필터를 사용한 경량화 부호화 방법을 제안하였었다. 하지만 이전 방법에서는 텍스처가 복잡한 영상의 경우, 필터적용에 따른 텍스처의 손실이 오류정정으로 얻는 이득보다 더 큰 경우가 발생하는 문제점이 있었다. 따라서 본 논문에서는 선택적 미디언 필터에 복원영상과 보조정보 내 잡음에 관한 정보를 제공함으로써 필터링에 따른 텍스처의 손실을 최소화하는 향상된 경량화 부호화 방법을 제안한다. 실험결과는 이전 방법에 비해 최대 0.84dB에 이르는 성능향상을 보였다. In conventional video coding, encoder complexity is much higher than that of decoder. However, investigations for lightweight encoder to eliminate motion prediction/compensation claiming most complexity in encoder have recently become an important issue. The Wyner-Ziv coding is one of the representative schemes for the problem and, in this scheme, since encoder generates only parity bits of a current frame without performing any type of processes extracting correlation information between frames, it has an extremely simple structure compared to conventional coding techniques. However, in Wyner-Ziv coding, channel decoding errors occur when noisy side information is used in channel decoding process. These channel decoding errors appear more frequently, especially, when there is not enough correlation between frames to generate accurate side information and, as a result, those errors look like Salt & Pepper type noise in the reconstructed frame. Since this noise severely deteriorates subjective video quality even though such noise rarely occurs, previously we proposed a computationally extremely light encoding method based on selective median filter that corrects such noise using spatial correlation of a frame. However, in the previous method, there is a problem that loss of texture from filtering may exceed gain from error correction by the filter for video sequences having complex torture. Therefore, in this paper, we propose an improved lightweight encoding method that minimizes loss of texture detail from filtering by allowing information of texture and that of noise in side information to be utilized by the selective median filter. Our experiments have verified average PSNR gain of up to 0.84dB compared to the previous method.

Optimum linear coding in continuous-time communication systems with noisy side information at the decoder (Corresp.)

The problem of optimal coding in a communication system with a noiseless feedback link and with noisy side information at the decoder is treated. The message is taken as a Gaussian random variable and both the main and the side channels are assumed to be continuous white-Gaussian. A linear encoding-decoding scheme is developed which attains the well-known performance bounds for the special cases of i) noiseless side information and ii) no side information.

Optimal linear coding schemes for feedback communication with noisy side information (Corresp.)

Optimal linear coding schemes for feedback communication with noisy side information are derived. The performance of two schemes with side information are compared with the information-theoretic bounds. The side information at the transmitter Significantly reduces the mean-squared error. The corresponding continuous-time system is briefly mentioned.

A note on feedback communication with noisy side information at the receiver (Corresp.)

A feedback communication system which has noisy side information at the receiver is considered. The estimation scheme is a simple extension of the conventional one without side information. The resulting mean-squared error is compared with the rate-distortion bound obtained recently by Wyner and Ziv.