Artificial intelligence neural network apparatus and data classification method with visualized feature vector

1. An artificial intelligence neural network apparatus coupled with a visualized feature vector, comprising:

a labeled learning database having data of a feature vector composed of N elements;

a first feature vector image converter configured to visualize the data in the labeled learning database to form an imaged learning feature vector image database;

a deep-learned artificial intelligence neural network configured to use a learning feature vector image in the learning feature vector image database to perform an image classification operation;

an inputter configured to receive a test image, and generate test data based on the feature vector; and

a second feature vector image converter configured to visualize the test data and convert the visualized test data into a test feature vector image,

wherein the deep-learned artificial intelligence neural network is configured to determine a class of the test feature vector image, and

wherein the first feature vector image converter includes:

a pattern image storage configured to store pattern images of a relationship between element x i and other elements {x j |j=i+1, i+2, . . . , N} for data represented by a feature vector composed of the N elements {x i |i∈1, 2, . . . , N};

an address generator configured to calculate an address for reading the pattern image from the pattern image storage;

an element x i visualizer configured to obtain visualized cross correlation images {A ij |j=i+1, i+2, . . . , N} by reading a pattern image corresponding to the address generated from the address generator and mapping the pattern image read into a two-dimensional space;

a first addition operator configured to generate a local pattern image B i by synthesizing cross correlation images {A ij |j=i+1, i+2, . . . , N} obtained from the element x i visualizer; and

a second addition operator configured to generate a feature vector image by synthesizing a local pattern image {B i |i=1, 2, . . . , N−1} obtained from the first addition operator.

2. The artificial intelligence neural network apparatus of claim 1 , wherein the deep-learned artificial intelligence neural network is deep-learned by supervised learning using the learning feature vector image stored in the learning feature vector image database.

3. The artificial intelligence neural network apparatus of claim 1 , wherein the first addition operator further includes:

a multiplier configured to perform multiplication of weight W ij by the cross correlation image A ij ; and

an adder configured to perform addition to the bias b i ,

wherein the weight W ij and the bias b i are customized while being learned by a supervised learning of the deep-learned artificial intelligence neural network.

4. A processor implemented artificial intelligence neural network method, the method comprising:

visualizing data in a labeled learning database to form an imaged learning feature vector image database, wherein the labeled learning database has the data composed of N elements;

using a learning feature vector image in the learning feature vector image database to perform an image classification operation;

receiving a test image and generating test data based on the feature vector;

visualizing the test data and converting the visualized test data into a test feature vector image;

determining a class of the test feature vector image;

storing pattern images of a relationship between element x i and other elements {x j |j=i+1, i+2, . . . , N} for data represented by a feature vector composed of the N elements {x i |i ∈1, 2, . . . , N};

calculating an address for reading the pattern image from the pattern image storage;

obtaining visualized cross correlation images {A ij |j=i+1, i+2, . . ., N} by reading a pattern image corresponding to the address generated from the address generator and mapping the pattern image read into a two-dimensional space;

generating a local pattern image B i by synthesizing cross correlation images {A ij |j=i+1, i+2, . . . , N} obtained from the element x i visualizer; and

generating a feature vector image by synthesizing a local pattern image {B i |i=1, 2, . . . , N−1} obtained from the first addition operator.

5. The method of claim 4 , further including:

performing multiplication of weight W ij by the cross correlation image A ij ; and

performing addition to the bias b i , wherein the weight W ij and the bias b i are customized while being learned by a supervised learning of a deep-learned artificial intelligence neural network.