Recovery of Linear Components: Reduced Complexity Autoencoder Designs
- URL: http://arxiv.org/abs/2012.07543v1
- Date: Mon, 14 Dec 2020 14:08:20 GMT
- Title: Recovery of Linear Components: Reduced Complexity Autoencoder Designs
- Authors: Federico Zocco and Se\'an McLoone
- Abstract summary: We present an approach called Recovery of Linear Components (RLC), which serves as a middle ground between linear and non-linear dimensionality reduction techniques.
With the aid of synthetic and real world case studies, we show that the RLC, when compared with an autoencoder of similar complexity, shows higher accuracy, similar to robustness to overfitting, and faster training times.
- Score: 0.951828574518325
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reducing dimensionality is a key preprocessing step in many data analysis
applications to address the negative effects of the curse of dimensionality and
collinearity on model performance and computational complexity, to denoise the
data or to reduce storage requirements. Moreover, in many applications it is
desirable to reduce the input dimensions by choosing a subset of variables that
best represents the entire set without any a priori information available.
Unsupervised variable selection techniques provide a solution to this second
problem. An autoencoder, if properly regularized, can solve both unsupervised
dimensionality reduction and variable selection, but the training of large
neural networks can be prohibitive in time sensitive applications. We present
an approach called Recovery of Linear Components (RLC), which serves as a
middle ground between linear and non-linear dimensionality reduction
techniques, reducing autoencoder training times while enhancing performance
over purely linear techniques. With the aid of synthetic and real world case
studies, we show that the RLC, when compared with an autoencoder of similar
complexity, shows higher accuracy, similar robustness to overfitting, and
faster training times. Additionally, at the cost of a relatively small increase
in computational complexity, RLC is shown to outperform the current
state-of-the-art for a semiconductor manufacturing wafer measurement site
optimization application.
Related papers
- Symplectic Autoencoders for Model Reduction of Hamiltonian Systems [0.0]
It is crucial to preserve the symplectic structure associated with the system in order to ensure long-term numerical stability.
We propose a new neural network architecture in the spirit of autoencoders, which are established tools for dimension reduction.
In order to train the network, a non-standard gradient descent approach is applied.
arXiv Detail & Related papers (2023-12-15T18:20:25Z) - CORE: Common Random Reconstruction for Distributed Optimization with
Provable Low Communication Complexity [110.50364486645852]
Communication complexity has become a major bottleneck for speeding up training and scaling up machine numbers.
We propose Common Om REOm, which can be used to compress information transmitted between machines.
arXiv Detail & Related papers (2023-09-23T08:45:27Z) - Fundamental Limits of Two-layer Autoencoders, and Achieving Them with
Gradient Methods [91.54785981649228]
This paper focuses on non-linear two-layer autoencoders trained in the challenging proportional regime.
Our results characterize the minimizers of the population risk, and show that such minimizers are achieved by gradient methods.
For the special case of a sign activation function, our analysis establishes the fundamental limits for the lossy compression of Gaussian sources via (shallow) autoencoders.
arXiv Detail & Related papers (2022-12-27T12:37:34Z) - Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A
Memory-Efficient Inverse Problem Solver [26.87738024952936]
In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements.
We propose an LU algorithm with shallow equilibrium regularizers (L)
These implicit models are as expressive as deeper convolutional networks, but far more memory efficient during training.
arXiv Detail & Related papers (2022-10-10T19:50:37Z) - An Accelerated Doubly Stochastic Gradient Method with Faster Explicit
Model Identification [97.28167655721766]
We propose a novel doubly accelerated gradient descent (ADSGD) method for sparsity regularized loss minimization problems.
We first prove that ADSGD can achieve a linear convergence rate and lower overall computational complexity.
arXiv Detail & Related papers (2022-08-11T22:27:22Z) - Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm
of spatio-temporal data [4.996878640124385]
We propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatialtemporal data.
NIF consists of two modified multilayer perceptrons (i) ShapeNet, which isolates and represents the spatial complexity (i) ShapeNet, which accounts for any other input measurements, including parametric dependencies, time, and sensor measurements.
We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatial-temporal dynamics, efficient many-spatial-temporal generalization, and improved performance for sparse
arXiv Detail & Related papers (2022-04-07T05:02:58Z) - SreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm [60.61943386819384]
Existing implementations of KRR require that all the data is stored in the main memory.
We propose StreaMRAK - a streaming version of KRR.
We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum.
arXiv Detail & Related papers (2021-08-23T21:03:09Z) - Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge
Computing: A Contextual-Bandit Approach [81.5261621619557]
We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC)
We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer.
Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
arXiv Detail & Related papers (2021-08-09T08:45:47Z) - Sample-Efficient Reinforcement Learning Is Feasible for Linearly
Realizable MDPs with Limited Revisiting [60.98700344526674]
Low-complexity models such as linear function representation play a pivotal role in enabling sample-efficient reinforcement learning.
In this paper, we investigate a new sampling protocol, which draws samples in an online/exploratory fashion but allows one to backtrack and revisit previous states in a controlled and infrequent manner.
We develop an algorithm tailored to this setting, achieving a sample complexity that scales practicallyly with the feature dimension, the horizon, and the inverse sub-optimality gap, but not the size of the state/action space.
arXiv Detail & Related papers (2021-05-17T17:22:07Z) - Reservoir Based Edge Training on RF Data To Deliver Intelligent and
Efficient IoT Spectrum Sensors [0.6451914896767135]
We propose a processing architecture that supports general machine learning algorithms on compact mobile devices.
Deep Delay Loop Reservoir Computing (DLR) delivers reductions in form factor, hardware complexity and latency, compared to the State-of-the-Art (SoA)
We present DLR architectures composed of multiple smaller loops whose state vectors are linearly combined to create a lower dimensional input into Ridge regression.
arXiv Detail & Related papers (2021-04-01T20:08:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.