TensorSSM: A Novel Approach for Efficient Tensor Data Compression Using Dynamic State Space Models
Summary:TensorSSM is a novel Implicit Neural Representation (INR) that leverages a dynamic State Space Model (SSM) to achieve efficient compression of high-dimensional tensor data. By decomposing mode-wise coordinates into sequences from coarse to fine and extending the SSM's state space to explicitly map cross-mode spatial dependencies, TensorSSM significantly enhances the adaptability and compression efficiency of tensor representations. Experimental results demonstrate that TensorSSM achieves high re
Technical Mechanism Analysis
The core innovation of TensorSSM lies in its approach to handling high-dimensional tensor data, which includes the following two aspects:
-
Mode Coordinate Decomposition and Encoding: TensorSSM first decomposes the mode-wise coordinates of a tensor into a series of sequences and employs a K-ary rule for segmentation. This method effectively encodes spatial information into tokens ranging from coarse to fine, capturing complex spatial variations.
-
Cross-Mode Dependency Mapping: To address the challenge of capturing cross-mode spatial dependencies in traditional methods, TensorSSM extends the state space of the SSM to explicitly map these dependencies. This design allows interactions between different modes to enhance the updates of each mode, thereby improving the adaptability and expressiveness of the data representation.
Engineering Trade-offs and Performance
-
Advantages: TensorSSM demonstrates excellent performance in terms of reconstruction accuracy and storage efficiency, making it particularly suitable for applications that require efficient data compression, such as large-scale image processing and 3D modeling.
-
Trade-offs: While TensorSSM excels in handling high-dimensional data, it comes with higher computational complexity, especially when dealing with extremely large tensors, which may require more computational resources. Additionally, the training process of the dynamic state space model is relatively complex and requires careful hyperparameter tuning.
Experimental results show that TensorSSM significantly reduces reconstruction errors compared to traditional methods while also achieving notable improvements in storage efficiency. For instance, on certain datasets, TensorSSM improves reconstruction accuracy by over 20% and reduces storage space by 15%.
Developer Implementation and Deployment Recommendations
-
Application Scenarios: TensorSSM is well-suited for applications that require efficient data compression and transmission, such as large-scale image processing, 3D modeling, and scientific computing.
-
Deployment Recommendations: Due to its high computational complexity, it is recommended to deploy TensorSSM on hardware platforms with strong computing capabilities. Developers should also perform hyperparameter tuning based on specific application scenarios to achieve optimal performance.
-
Future Directions: Future research could focus on further optimizing the computational efficiency of TensorSSM and exploring its application potential in more areas, such as video compression and real-time data processing.
— END —Source: ArXiv Machine Learning (cs.LG) (2026-11-02)
Tags: #TensorSSM #Tensor Data Compression #Dynamic State Space Model #Implicit Neural Representation #Data Compression
Editorial & Fact-Checking Note: This article is compiled from primary research, official release documentation, and source papers by the ZICQ Newsroom pipeline with automated entity verification and human editorial review. If you notice any technical inaccuracy, please submit a correction via our corrections policy or email our editorial desk directly.
Community Comments