Paper Reading AI Learner

Multilinear Compressive Learning

2019-05-17 21:04:44
Dat Thanh Tran, Mehmet Yamac, Aysen Degerli, Moncef Gabbouj, Alexandros Iosifidis

Abstract

Compressive Learning is an emerging topic that combines signal acquisition via compressive sensing and machine learning to perform inference tasks directly on a small number of measurements. Many data modalities naturally have a multi-dimensional or tensorial format, with each dimension or tensor mode representing different features such as the spatial and temporal information in video sequences or the spatial and spectral information in hyperspectral images. However, in existing compressive learning frameworks, the compressive sensing component utilizes either random or learned linear projection on the vectorized signal to perform signal acquisition, thus discarding the multi-dimensional structure of the signals. In this paper, we propose Multilinear Compressive Learning, a framework that takes into account the tensorial nature of multi-dimensional signals in the acquisition step and builds the subsequent inference model on the structurally sensed measurements. Our theoretical complexity analysis shows that the proposed framework is more efficient compared to its vector-based counterpart in both memory and computation requirement. With extensive experiments, we also empirically show that our Multilinear Compressive Learning framework outperforms the vector-based framework in object classification and face recognition tasks, and scales favorably when the dimensionalities of the original signals increase, making it highly efficient for high-dimensional multi-dimensional signals.

Abstract (translated)

压缩学习是一个新兴的课题,它将通过压缩传感的信号采集和机器学习相结合,直接在少量测量上执行推理任务。许多数据模式自然具有多维或张量格式,每个维度或张量模式代表不同的特征,如视频序列中的时空信息或高光谱图像中的空间和光谱信息。然而,在现有的压缩学习框架中,压缩感测元件利用矢量化信号上的随机或学习线性投影来进行信号采集,从而抛弃了信号的多维结构。本文提出了一种多线性压缩学习框架,该框架考虑了采集过程中多维信号的张量性质,并在结构传感测量的基础上建立了后续的推理模型。我们的理论复杂性分析表明,与基于向量的框架相比,该框架在内存和计算需求方面更为有效。通过大量的实验,我们也从经验上证明了我们的多线性压缩学习框架在目标分类和人脸识别任务上优于基于矢量的学习框架,并且当原始信号的维数增加时,能够很好地扩展,使其对于高维多维信号的处理效率更高。LS

URL

https://arxiv.org/abs/1905.07481

PDF

https://arxiv.org/pdf/1905.07481.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot