Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Robust PCA with compressed data
The robust principal component analysis (RPCA) problem seeks to separate low-rank trends from sparse outlierswithin a data matrix, that is, to approximate a $n\times d$ matrix $D$ as the sum of a low-rank matrix $L$ and …
Data CompressionThe Utility of Clustering in Prediction Tasks
We explore the utility of clustering in reducing error in various prediction tasks. Previous work has hinted at the improvement in prediction accuracy attributed to clustering algorithms if used to pre-process the data. …
ClusteringData CompressionPredictionArtificial general intelligence through recursive data compression and grounded reasoning: a position paper
This paper presents a tentative outline for the construction of an artificial, generally intelligent system (AGI). It is argued that building a general data compression algorithm solving all problems up to a complexity t…
Data CompressionInductive BiasPositionSparse Multi-layer Image Approximation: Facial Image Compression
We propose a scheme for multi-layer representation of images. The problem is first treated from an information-theoretic viewpoint where we analyze the behavior of different sources of information under a multi-layer dat…
Data CompressionDictionary LearningImage CompressionReduced Basis Decomposition: a Certified and Fast Lossy Data Compression Algorithm
Dimension reduction is often needed in the area of data mining. The goal of these methods is to map the given high-dimensional data into a low-dimensional space preserving certain properties of the initial data. There ar…
Data CompressionDimensionality ReductionImage Data Compression for Covariance and Histogram Descriptors
Covariance and histogram image descriptors provide an effective way to capture information about images. Both excel when used in combination with special purpose distance metrics. For covariance descriptors these metrics…
Data CompressionGeneral ClassificationCompressive Mining: Fast and Optimal Data Mining in the Compressed Domain
Real-world data typically contain repeated and periodic patterns. This suggests that they can be effectively represented and compressed using only a few coefficients of an appropriate basis (e.g., Fourier, Wavelets, etc.…
ClusteringData CompressionUniversal Memcomputing Machines
We introduce the notion of universal memcomputing machines (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the sam…
Data CompressionSpatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression proced…
Brain Computer Interfacecompressed sensingData CompressionEEG+1Improving Bilayer Product Quantization for Billion-Scale Approximate Nearest Neighbors in High Dimensions
The top-performing systems for billion-scale high-dimensional approximate nearest neighbor (ANN) search are all based on two-layer architectures that include an indexing structure and a compressed datapoints layer. An in…
Data CompressionQuantizationAuthorship Analysis based on Data Compression
This paper proposes to perform authorship analysis using the Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries directly extracted from the written texts. The FCD computes a simi…
Data CompressionBayesian Properties of Normalized Maximum Likelihood and its Fast Computation
The normalized maximized likelihood (NML) provides the minimax regret solution in universal data compression, gambling, and prediction, and it plays an essential role in the minimum description length (MDL) method of sta…
Data CompressionPredictionAnamorphic transformation and its application to time-bandwidth compression
A general method for compressing the modulation time-bandwidth product of analog signals is introduced and experimentally demonstrated. As one of its applications, this physics-based signal grooming performs feature-sele…
Data CompressionNovel Factorization Strategies for Higher Order Tensors: Implications for Compression and Recovery of Multi-linear Data
In this paper we propose novel methods for compression and recovery of multilinear data under limited sampling. We exploit the recently proposed tensor- Singular Value Decomposition (t-SVD)[1], which is a group theoretic…
Data CompressionTensor DecompositionCompressive Sensing of Sparse Tensors
Compressive sensing (CS) has triggered enormous research activity since its first appearance. CS exploits the signal's sparsity or compressibility in a particular domain and integrates data compression and acquisition, t…
Compressive SensingData CompressionSMML estimators for exponential families with continuous sufficient statistics
The minimum message length principle is an information theoretic criterion that links data compression with statistical inference. This paper studies the strict minimum message length (SMML) estimator for $d$-dimensional…
Data CompressionCompressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware
Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are en…
compressed sensingData CompressionEEGElectroencephalogram (EEG)Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable. As an eme…
compressed sensingCPUData CompressionDeciphering subsampled data: adaptive compressive sampling as a principle of brain communication
A new algorithm is proposed for a) unsupervised learning of sparse representations from subsampled measurements and b) estimating the parameters required for linearly reconstructing signals from the sparse codes. We veri…
Data Compression