Multi-Modal Data Feature Detection via Dimensional Reduction
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Solution Overview
Problem
Existing computer vision algorithms face challenges in identifying features in multi-modal and multi-dimensional data, particularly in datasets with many-to-many data relationships, where traditional dimensional reduction and feature detection methods are insufficient.
Innovation Solution
The method involves accessing multi-modal and multi-dimensional data, performing dimensional reduction to create a plottable dataset, and dynamically modifying perspective views of the plotted data to identify visually detectable features using a computer vision algorithm. Once a feature is identified, the original data is sampled to determine the contributing data relationship, which triggers a re-training of the computer vision algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If dimensional reduction operations are performed on multi-dimensional data, then the data becomes plottable in a coordinate system, but the ability to identify features in the original multi-dimensional relationships is lost
Solution Approach 1:
The patent plots multi-dimensional data points in a two-dimensional coordinate system while preserving their multi-dimensional relationships through spatial positioning. Each data point's position in the 2D plot encodes its relationships with other data points across multiple dimensions, allowing visual feature detection without losing the underlying multi-dimensional structure.
2Device complexity
If traditional computer vision algorithms are used on dimensionally reduced data, then processing is simplified, but detection precision of features in complex multi-modal data decreases
Solution Approach 1:
The patent replaces traditional algorithmic feature detection with visual feature detection by computer vision algorithms operating on plotted data. The mechanical/computational process of analyzing multi-dimensional arrays is substituted with optical/visual pattern recognition on 2D plots, leveraging human and machine visual processing capabilities to identify features that are difficult to detect through conventional computational methods.
3Measurement precision
If the computer vision algorithm is re-trained frequently to improve feature detection, then detection precision improves, but processing time increases
Solution Approach 1:
The patent implements a feedback mechanism where the computer vision algorithm detects features in plotted data, identifies relationships between data points, and triggers targeted re-training only when specific feature detection challenges are encountered. This feedback loop ensures the algorithm is re-trained based on actual performance needs rather than continuously, optimizing the balance between detection precision and processing time.
Data Source
AI summary
Techniques for identifying features within a dataset are disclosed. An original set of data is accessed. This data set is dimensionally reduced by performing a dimensional reduction operation. The dimensionally reduced data set is plottable in a coordinate system as a result of the dimensional reduction operation being performed. The dimensionally reduced data set is plotted in the coordinate system, resulting in generation of a visual plot of the dimensionally reduced data set. Perspective views of the plot are modified in an attempt to identify a feature. In response to a particular feature being identified, the original set of data is sampled to identify a data relationship that exists within the original set of data. This data relationship is one that contributed to the feature being detectable.