Unsupervised Feature Learning Via Sparse Hierarchical ...

Specifically, I will first describe how efficient sparse coding algorithms — which represent each input example using a small number of basis vectors — can be ...

Unsupervised feature learning via sparse hierarchical ...

When applied to images, this method automatically learns features that correspond to objects and decompositions of objects into object-parts. These features ...

[PDF] Unsupervised feature learning via sparse hierarchical ...

This work describes how efficient sparse coding algorithms — which represent each input example using a small number of basis vectors — can be used to learn ...

Toyota AI Seminar: Unsupervised Feature Learning via Sparse ...

Toyota AI Seminar: Unsupervised Feature Learning via Sparse Hierarchical Representations. Honglak LeeAssistant ProfessorUniversity of Michigan, CSE. WHEN ...

Unsupervised feature learning via sparse hierarchical ... - WorldCat

Machine learning has proved a powerful tool for artificial intelligence and data mining problems. However, its success has usually relied on having a good ...

Unsupervised feature learning for 3D scene labeling - IEEE Xplore

This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a hierarchical sparse coding technique for learning features from 3D ...

Unsupervised Hierarchical Feature Selection on Networked Data

algorithm by selecting latent features with sparse learning [28]. Following this approach, we choose to embed feature selection into a low-rank matrix construc-.

Unsupervised learning of hierarchical representations with ...

Sparse feature learning for deep belief networks. In Advances in Neural ... Deep learning via semi-supervised embedding. In Proceedings of the ...

Sparse Feature Learning for Deep Belief Networks

One cause for the recent resurgence of interest in unsupervised learning is the ability to produce deep feature hierarchies by stacking unsupervised modules on ...

Unsupervised Feature Learning for RGB-D Based Object Recognition

HMP uses sparse coding to learn hierarchical feature ... Learning Image Representations from the Pixel Level via. Hierarchical Sparse Coding.

Unsupervised Feature Learning by Deep Sparse Coding

al. [19] propose to compress the sparse codes into low-dimensional dense features. Multipath sparse coding [2] combines a collection of hierarchical sparse ...

Unsupervised Feature Learning in Computer Vision

In the supervised setting, these hierarchies are trained to solve specific problems by minimizing an objective function of the data and problem specific label ...

Learning Hierarchical Features Using Sparse Self-organizing Map ...

This process can be performed by applying two operations of coding and pooling. In coding operation, an appropriate codebook is learned and better adapted from ...

Stacked Autoencoders for Unsupervised Feature Learning and ...

Examples of our model of two-layer stacked sparse autoencoder networks for learning hierarchical ... size visual feature set learned by unsupervised sparse ...

[1606.01535] What is the Best Feature Learning Procedure in ... - arXiv

... sparse convolutional feature hierarchies: unsupervised pre-training followed by supervised fine-tuning. Recent results suggest that these ...

Unsupervised Feature Learning for RGB-D Based Object Recognition

HMP uses sparse coding to learn hierarchical feature representations from raw RGB-D data in an unsupervised way. ... by a sparse, linear combination of.

Unsupervised Learning of Feature Hierarchies - Semantic Scholar

Unsupervised feature learning via sparse hierarchical representations · Honglak Lee. Computer Science. 2010. TLDR. This work describes how efficient sparse ...

Unsupervised feature selection using sparse manifold learning

The subtle point is that the performance of auto-encoder to obtain the most discriminative features is significantly affected by decoding phase. To address ...

CS 7300: Unsupervised Feature Learning

It focuses on development of machine learning features, considering the feature hierarchies from ... Unsupervised Learning Algorithms, by Celebi and Aydin ...

Unsupervised Feature Learning for RGB-D Based Object Recognition

... Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical ... Learning Image Representations from the Pixel Level via Hierarchical Sparse Coding.