ROBUST CLASSIFICATION BY PRE-CONDITIONED LASSO AND TRANSDUCTIVE DIFFUSION COMPONENT ANALYSIS

Number: 20180089580
Published: 2018-03-29
Filed: 2016-09-27
Assignee: Disney Enterprises, Inc.
Inventors: FU; Yanwei et al.
CPC: G06F16/285; G06N20/00; G06F16/51; G06F16/55; G06F16/9024; G06F16/583
Verdict: Set aside generic ML classification algorithm, no creative hook
Source: Google Patents · FreePatentsOnline

Abstract

Techniques are disclosed for identifying and filtering outliers from a sample set of data prior to training a classifier on an object using the sample set. A data set including a plurality of samples used to train a classification model is retrieved. The samples in the data set have a feature dimensionality. A graph of the data set is built. Each node in the graph corresponds to a sample in the data set and edges connecting the nodes correspond to a measure of similarity between the nodes. The feature dimensionality of the sample data set is reduced based on a topology of the graph. One or more outliers in the data set are identified based on the reduced feature dimensionality.

Background

BACKGROUNDField

Embodiments presented herein generally relate to data classification (e.g., image classification, audio classification, etc.), and more specifically, to identifying and filtering outliers from a data set.Description of the Related Art

Many modern computer data recognition approaches use supervised learning techniques that rely on a large set of labeled data to train classification models. Such data sets have been collected from the web by searching for query terms relevant to a particular object label and verifying consistency through crowdsourced labeling, i.e., soliciting verification of the label to a data set from a large group of people. Such an approach is often costly and difficult to scale. For example, image data sets often include multiple labels, and each image in the set needs to be verified or labeled by a group of individual annotators.

To address such difficulties of image data sets, automated methods may collect, cluster and annotate the images based on characteristics or features found in the images. The annotated clusters can be used to train a classifier to recognize one or more objects in subsequently presented example images where examples of that object appeared in the annotated clusters on a given image-based object (i.e., an object that appears in the image). However, the resulting annotated clusters tend to include a number of outliers, i.e., images that do not correspond to an instance of the object associated with t

Claims

1. A computer-implemented method, comprising: retrieving a data set including a plurality of samples used to train a classification model, wherein the samples in the data set have a feature dimensionality; building, by operation of a processor executing one or more applications, a graph data structure of the data set, wherein each node in the graph data structure corresponds to a sample in the data set and wherein an edge connecting a first and second node in the graph data structure corresponds to a measure of similarity between the first and second node; reducing the feature dimensionality of the sample data set based on a topology of the graph data structure; and identifying one or more outliers in the plurality of samples based on the reduced feature dimensionality. 8. A non-transitory computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation comprising: retrieving a data set including a plurality of samples used to train a classification model, wherein the samples in the data set have a feature dimensionality; building a graph data structure of the data set, wherein each node in the graph data structure corresponds to a sample in the data set and wherein an edge connecting a first and second node in the graph data structure corresponds to a measure of similarity between the first and second node; reducing the feature dimensionality of the sample data set based on a topology of the graph data structure; and identifying one or more outliers in the plurality of samples based on the reduced feature dimensionality. 15. A system, comprising: a processor; and a memory storing program code, which, when executed on the processor, performs an operation comprising: retrieving a data set including a plurality of samples used to train a classification model, wherein the samples in the data set have a feature dimensionality; building a graph data structure of the data set, wherein each node in the graph data structure corresponds to a sample in the data set and wherein an edge connecting a first and second node in the graph data structure corresponds to a measure of similarity between the first and second node; reducing the feature dimensionality of the sample data set based on a topology of the graph data structure; and identifying one or more outliers in the plurality of samples based on the reduced feature dimensionality.