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Found 18 papers, 1 papers with code
Deep Dictionary Learning with An Intra-class Constraint
Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage.
Boosting Semantic Segmentation via Feature Enhancement
Semantic segmentation aims to map each pixel of an image into its correspond-ing semantic label.
RegNet: Self-Regulated Network for Image Classification
The ResNet and its variants have achieved remarkable successes in various computer vision tasks.
Ranked #3 on
Medical Image Classification
on NCT-CRC-HE-100K
IGD Indicator-based Evolutionary Algorithm for Many-objective Optimization Problems
Inverted Generational Distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multi- and many-objective evolutionary algorithms.
Improved Regularity Model-based EDA for Many-objective Optimization
Finally, by assigning the Pareto-optimal solutions to the uniformly distributed reference vectors, a set of solutions with excellent diversity and convergence is obtained.
Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations
Specifically, error classification rate on MNIST with $1. 15\%$ is reached by the proposed algorithm consistently, which is a very promising result against state-of-the-art unsupervised DL algorithms.
Deep Sparse Subspace Clustering
In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC).
Long Concept Query on Conceptual Taxonomies
However, as such increase of recall often invites false positives and decreases precision in return, we propose the following two techniques: First, we identify concepts with different relatedness to generate linear orderings and pairwise ordering constraints.
Connections Between Nuclear Norm and Frobenius Norm Based Representations
A lot of works have shown that frobenius-norm based representation (FNR) is competitive to sparse representation and nuclear-norm based representation (NNR) in numerous tasks such as subspace clustering.
Automatic Subspace Learning via Principal Coefficients Embedding
In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i. e., automatic subspace learning), and 2) how to learn the underlying subspace in the presence of Gaussian noise (i. e., robust subspace learning).
Symmetric low-rank representation for subspace clustering
We propose a symmetric low-rank representation (SLRR) method for subspace clustering, which assumes that a data set is approximately drawn from the union of multiple subspaces.
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image.
Subspace clustering using a symmetric low-rank representation
In this paper, we propose a low-rank representation with symmetric constraint (LRRSC) method for robust subspace clustering.
A Unified Framework for Representation-based Subspace Clustering of Out-of-sample and Large-scale Data
In this paper, we propose a unified framework which makes representation-based subspace clustering algorithms feasible to cluster both out-of-sample and large-scale data.
Scalable Sparse Subspace Clustering
To address the problems, this paper proposes out-of-sample extension of SSC, named as Scalable Sparse Subspace Clustering (SSSC), which makes SSC feasible to cluster large scale data sets.
Locally linear representation for image clustering
There are two popular schemes to construct a similarity graph, i. e., pairwise distance based scheme and linear representation based scheme.
Learning Locality-Constrained Collaborative Representation for Face Recognition
The model of low-dimensional manifold and sparse representation are two well-known concise models that suggest each data can be described by a few characteristics.
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i. e., intra-subspace data points).
